Systems and methods for monitoring consumption

ABSTRACT

A wearable apparatus may automatically monitor consumption by a user of the wearable apparatus by analyzing images captured from an environment of the user. The wearable apparatus may include at least one image capture device configured to capture a plurality of images from an environment of the user of the wearable apparatus. The wearable apparatus may also include at least one processing device configured to: analyze the plurality of images to detect a consumable product represented in at least one of the plurality of images; based on the detection of the consumable product represented in at least one of the plurality of images, analyze one or more of the plurality of images to determine a type indicator associated with the detected consumable product; analyze the one or more of the plurality of images to estimate an amount of the consumable product consumed by the user; determine a feedback based on the type indicator the detected consumable product and the estimated amount of the consumable product consumed by the user; and cause the feedback to be outputted to the user.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/631,963, filed Feb. 19, 2018, which isincorporated herein by reference in its entirety.

BACKGROUND Technical Field

This disclosure generally relates to devices and methods for capturingand processing images from an environment of a user, and usinginformation derived from captured images.

Background Information

Today, technological advancements make it possible for wearable devicesto automatically capture images and store information that is associatedwith the captured images. Certain devices have been used to digitallyrecord aspects and personal experiences of one's life in an exercisetypically called “lifelogging.” Some individuals log their life so theycan retrieve moments from past activities, for example, social events,trips, etc. Lifelogging may also have significant benefits in otherfields (e.g., business, fitness and healthcare, and social research).Lifelogging devices, while useful for tracking daily activities, may beimproved with capability to enhance one's interaction in his environmentwith feedback and other advanced functionality based on the analysis ofcaptured image data.

Even though users can capture images with their smartphones and somesmartphone applications can process the captured images, smartphones maynot be the best platform for serving as lifelogging apparatuses in viewof their size and design. Lifelogging apparatuses should be small andlight, so they can be easily worn. Moreover, with improvements in imagecapture devices, including wearable apparatuses, additionalfunctionality may be provided to assist users in navigating in andaround an environment, identifying persons and objects they encounter,and providing feedback to the users about their surroundings andactivities. Therefore, there is a need for apparatuses and methods forautomatically capturing and processing images to provide usefulinformation to users of the apparatuses, and for systems and methods toprocess and leverage information gathered by the apparatuses.

SUMMARY

Embodiments consistent with the present disclosure provide devices andmethods for automatically capturing and processing images from anenvironment of a user, and systems and methods for processinginformation related to images captured from the environment of the user.

In an embodiment, a wearable apparatus may automatically monitorconsumption by a user of the wearable apparatus by analyzing imagescaptured from an environment of the user. The wearable apparatus mayinclude at least one image capture device configured to capture aplurality of images from an environment of the user of the wearableapparatus; and at least one processing device configured to: analyze theplurality of images to detect a consumable product represented in atleast one of the plurality of images; based on the detection of theconsumable product represented in at least one of the plurality ofimages, analyze one or more of the plurality of images to determine atype indicator associated with the detected consumable product; analyzethe one or more of the plurality of images to estimate an amount of theconsumable product consumed by the user; determine a feedback based onthe type indicator of the detected consumable product and the estimatedamount of the consumable product consumed by the user; and cause thefeedback to be outputted to the user.

In another embodiment, a method may automatically monitor consumption bya user of a wearable apparatus. The method may include capturing, by thewearable apparatus, one or more images from an environment of the user;analyzing the images to detect a consumable product represented in atleast one of the one or more images; determining a type of the detectedconsumable product: analyzing the images to estimate an amount of theconsumable product consumed by the user, determining a feedback based onthe type of the consumable product and the amount of the consumableproduct consumed by the user: and generating instructions for causingthe feedback to be output to the user.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processor and perform any of the methodsdescribed herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1A is a schematic illustration of an example of a user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1B is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1C is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 1D is a schematic illustration of an example of the user wearing awearable apparatus according to a disclosed embodiment.

FIG. 2 is a schematic illustration of an example system consistent withthe disclosed embodiments.

FIG. 3A is a schematic illustration of an example of the wearableapparatus shown in FIG. 1A.

FIG. 3B is an exploded view of the example of the wearable apparatusshown in FIG. 3A.

FIG. 4A is a schematic illustration of an example of the wearableapparatus shown in FIG. 1B from a first viewpoint.

FIG. 4B is a schematic illustration of the example of the wearableapparatus shown in FIG. 1B from a second viewpoint.

FIG. 5A is a block diagram illustrating an example of the components ofa wearable apparatus according to a first embodiment.

FIG. 5B is a block diagram illustrating an example of the components ofa wearable apparatus according to a second embodiment.

FIG. 5C is a block diagram illustrating an example of the components ofa wearable apparatus according to a third embodiment.

FIG. 6 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure.

FIG. 7 is a schematic illustration of an embodiment of a wearableapparatus including an orientable image capture unit.

FIG. 8 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 9 is a schematic illustration of a user wearing a wearableapparatus consistent with an embodiment of the present disclosure.

FIG. 10 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 11 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 12 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 13 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 14 is a schematic illustration of an embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure.

FIG. 15 is a schematic illustration of an embodiment of a wearableapparatus power unit including a power source.

FIG. 16 is a schematic illustration of an exemplary embodiment of awearable apparatus including protective circuitry.

FIG. 17 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure.

FIG. 18 is a schematic illustration of an image that may be analyzed bya wearable apparatus consistent with the present disclosure.

FIG. 19 is another schematic illustration of an exemplary image that maybe analyzed by a wearable apparatus consistent with the presentdisclosure.

FIG. 20 is a flowchart of a method for monitoring consumption of a useraccording to a disclosed embodiment consistent with the presentdisclosure.

FIG. 21 is a flowchart of a process generating a recommendationconsistent with the present disclosure.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

FIG. 1A illustrates a user 100 wearing an apparatus 110 that isphysically connected (or integral) to glasses 130, consistent with thedisclosed embodiments. Glasses 130 may be prescription glasses,magnifying glasses, non-prescription glasses, safety glasses,sunglasses, etc. Additionally, in some embodiments, glasses 130 mayinclude pans of a frame and earpieces, nosepieces, etc., and one or nolenses. Thus, in some embodiments, glasses 130 may function primarily tosupport apparatus 110, and/or an augmented reality display device orother optical display device. In some embodiments, apparatus 110 mayinclude an image sensor (not shown in FIG. 1A) for capturing real-timeimage data of the field-of-view of user 100. The term “image data”includes any form of data retrieved from optical signals in thenear-infrared, infrared, visible, and ultraviolet spectrums. The imagedata may include video clips and/or photographs.

In some embodiments, apparatus 110 may communicate wirelessly or via awire with a computing device 120. In some embodiments, computing device120 may include, for example, a smartphone, or a tablet, or a dedicatedprocessing unit, which may be portable (e.g., can be carried in a pocketof user 100). Although shown in FIG. 1A as an external device, in someembodiments, computing device 120 may be provided as part of wearableapparatus 110 or glasses 130, whether integral thereto or mountedthereon. In some embodiments, computing device 120 may be included in anaugmented reality display device or optical head mounted displayprovided integrally or mounted to glasses 130. In other embodiments,computing device 120 may be provided as part of another wearable orportable apparatus of user 100 including a wrist-strap, amultifunctional watch, a button, a clip-on, etc. And in otherembodiments, computing device 120 may be provided as part of anothersystem, such as an on-board automobile computing or navigation system. Aperson skilled in the art can appreciate that different types ofcomputing devices and arrangements of devices may implement thefunctionality of the disclosed embodiments. Accordingly, in otherimplementations, computing device 120 may include a Personal Computer(PC), laptop, an internet server, etc.

FIG. 1B illustrates user 100 wearing apparatus 110 that is physicallyconnected to a necklace 140, consistent with a disclosed embodiment.Such a configuration of apparatus 110 may be suitable for users that donot wear glasses some or all of the time. In this embodiment, user 100can easily wear apparatus 110, and take it off.

FIG. 1C illustrates user 100 wearing apparatus 110 that is physicallyconnected to a belt 150, consistent with a disclosed embodiment. Such aconfiguration of apparatus 110 may be designed as a belt buckle.Alternatively, apparatus 110 may include a clip for attaching to variousclothing articles, such as bell 150, necklace 140, or a vest, a pocket,a collar, a cap or hat or other portion of a clothing article.

FIG. 1D illustrates user 100 wearing apparatus 110 that is physicallyconnected to a wrist strap 160, consistent with a disclosed embodiment.Although the aiming direction of apparatus 110, according to thisembodiment, may not match the field-of-view of user 100, apparatus 110may include the ability to identify a hand-related trigger based on thetracked eye movement of a user 100 indicating that user 100 is lookingin the direction of the wrist strap 160. Wrist strap 160 may alsoinclude an accelerometer, a gyroscope, or other sensor for determiningmovement or orientation of a user's 100 hand for identifying ahand-related trigger.

FIG. 2 is a schematic illustration of an exemplary system 200 includinga wearable apparatus 110, worn by user 100, and an optional computingdevice 120 and/or a server 250 capable of communicating with apparatus110 via a network 240, consistent with disclosed embodiments. In someembodiments, apparatus 110 may capture and analyze image data, identifya hand-related trigger present in the image data, and perform an actionand or provide feedback to a user 100, based at least in part on theidentification of the hand-related trigger. In some embodiments,optional computing device 120 and/or server 250 may provide additionalfunctionality to enhance interactions of user 100 with his or herenvironment, as described in greater detail below.

According to the disclosed embodiments, apparatus 110 may include animage sensor system 220 for capturing real-time image data of thefield-of-view of user 100. In some embodiments, apparatus 110 may alsoinclude a processor 210 for controlling and performing the disclosedfunctionality of apparatus 110, such as to control the capture of imagedata, analyze the image data, and perform an action and/or output afeedback based on a hand-related trigger identified in the image data.According to the disclosed embodiments, a hand-related trigger mayinclude a gesture performed by user 100 involving a portion of a hand ofuser 100. Further, consistent with some embodiments, a hand-relatedtrigger may include a wrist-related trigger. In some embodiments, thehand-related trigger may include an indicator of consumption, such asthe presence of a utensil in a hand, the presence of a consumableproduct in a hand, motion of the hand to and from the mouth, or otheraction associated with consumption. Additionally, in some embodiments,apparatus 110 may include a feedback outputting unit 230 for producingan output of information to user 100. Feedback outputting unit 230 mayinclude one or more vibration devices, e.g., a vibration motor, aspeaker, or a display.

As discussed above, apparatus 110 may include an image sensor 220 forcapturing image data. The term “image sensor” refers to a device capableof detecting and converting optical signals in the near-infrared,infrared, visible, and ultraviolet spectrums into electrical signals.The electrical signals may be used to form an image or a video stream(i.e., image data) based on the detected signal. The term “image data”includes any form of data retrieved from optical signals in thenear-infrared, infrared, visible, and ultraviolet spectrums. Examples ofimage sensors may include semiconductor charge-coupled devices (CCD),active pixel sensors in complementary metal oxide semiconductor (CMOS),or N-type metal-oxide-semiconductor (NMOS, Live MOS). In some cases,image sensor 220 may be part of a camera included in apparatus 110.

Apparatus 110 may also include a processor 210 for controlling imagesensor 220 to capture image data and for analyzing the image dataaccording to the disclosed embodiments. As discussed in further detailbelow with respect to FIG. 5A, processor 210 may include a “processingdevice” for performing logic operations on one or more inputs of imagedata and other data according to stored or accessible softwareinstructions providing desired functionality. In some embodiments,processor 210 may also control feedback outputting unit 230 to providefeedback to user 100 including information based on the analyzed imagedata and the stored software instructions. As the term is used herein, a“processing device” may access memory where executable instructions arestored or, in some embodiments, a “processing device” itself may includeexecutable instructions (e.g., stored in memory included in theprocessing device).

In some embodiments, the information or feedback information provided touser 100 may include time information. The time information may includeany information related to a current time of day and, as describedfurther below, may be presented in any sensory perceptive manner. Insome embodiments, time information may include a current time of day ina preconfigured format (e.g., 2:30 pm or 14:30). Time information mayinclude the lime in the user's current time zone (e.g., based on adetermined location of user 100), as well as an indication of the timezone and/or a time of day in another desired location. In someembodiments, time information may include a number of hours or minutesrelative to one or more predetermined times of day. For example, in someembodiments, time information may include an indication that three hoursand fifteen minutes remain until a particular hour (e.g., until 6:00pm), or some other predetermined time. Time information may also includea duration of time passed since the beginning of a particular activity,such as the start of a meeting or the start of a jog, or any otheractivity. In some embodiments, the activity may be determined based onanalyzed image data. In other embodiments, time information may alsoinclude additional information related to a current time and one or moreother routine, periodic, or scheduled events. For example, timeinformation may include an indication of the number of minutes remaininguntil the next scheduled event, as may be determined from a calendarfunction or other information retrieved from computing device 120 orserver 250, as discussed in further detail below.

Feedback outputting unit 230 may include one or more feedback systemsfor providing the output of information to user 100. In the disclosedembodiments, the audible or visual feedback may be provided via any typeof connected audible or visual system or both. Feedback of informationaccording to the disclosed embodiments may include audible feedback touser 100 (e.g., using a Bluetooth® or other wired or wirelesslyconnected speaker, or a bone conduction headphone). Feedback outputtingunit 230 of some embodiments may additionally or alternatively produce avisible output of information to user 100, for example, as part of anaugmented reality display projected onto a lens of glasses 130 orprovided via a separate heads up display in communication with apparatus110, such as a display 260 provided as part of computing device 120,which may include an onboard automobile heads up display, an augmentedreality device, a virtual reality device, a smartphone, PC, table, etc.In some embodiments, feedback information may include providingvibrational or other tactile feedback to user 100 (e.g., using avibrating device within or in communication with feedback outputtingunit 230).

The term “computing device” refers to a device including a processor ora processing unit and having computing capabilities. Some examples ofcomputing device 120 include a PC, laptop, tablet, or other computingsystems such as an on-board computing system of an automobile, forexample, each configured to communicate directly with apparatus 110 orserver 250 over network 240. Another example of computing device 120includes a smartphone having a display 260. In some embodiments,computing device 120 may be a computing system configured particularlyfor apparatus 110, and may be provided integral to apparatus 110 ortethered thereto. Apparatus 110 can also connect to computing device 120over network 240 via any known wireless standard (e.g., Wi-Fi,Bluetooth®, etc.), as well as near-field capacitive coupling, and othershort range wireless techniques, or via a wired connection. In anembodiment in which computing device 120 is a smartphone, computingdevice 120 may have a dedicated application installed therein. Forexample, user 100 may view on display 260 data (e.g., images, videoclips, extracted information, feedback information, etc.) that originatefrom or are triggered by apparatus 110. In addition, user 100 may selectpart of the data for storage in server 250.

Network 240 may be a shared, public, or private network, may encompass awade area or local area, and may be implemented through any suitablecombination of wired and/or wireless communication networks. Network 240may further comprise an intranet or the Internet. In some embodiments,network 240 may include short range or near-field wireless communicationsystems for enabling communication between apparatus 110 and computingdevice 120 provided in close proximity to each other, such as on or neara user's person, for example. Apparatus 110 may establish a connectionto network 240 autonomously, for example, using a wireless module (e.g.,Wi-Fi, cellular). In some embodiments, apparatus 110 may use thewireless module when being connected to an external power source, toprolong battery life. Further, communication between apparatus 110 andserver 250 may be accomplished through any suitable communicationchannels, such as, for example, a telephone network, an extranet, anintranet, the Internet, satellite communications, off-linecommunications, wireless communications, transponder communications, alocal area network (LAN), a wide area network (WAN), and a virtualprivate network (VPN).

As shown in FIG. 2, apparatus 110 may transfer or receive data to/fromserver 250 via network 240. In the disclosed embodiments, the data beingreceived from server 250 and/or computing device 120 may includenumerous different types of information based on the analyzed imagedata, including information related to a commercial product, or aperson's identity, an identified landmark, and any other informationcapable of being stored in or accessed by server 250. In someembodiments, data may be received and transferred via computing device120. Server 250 and/or computing device 120 may retrieve informationfrom different data sources (e.g., a user specific database or a user'ssocial network account or other account, the Internet, and other managedor accessible databases) and provide information to apparatus 110related to the analyzed image data and a recognized trigger according tothe disclosed embodiments. In some embodiments, calendar-relatedinformation retrieved from the different data sources may be analyzed toprovide certain time information or a time-based context for providingcertain information based on the analyzed image data.

An example of wearable apparatus 110 incorporated with glasses 130according to some embodiments (as discussed in connection with FIG. 1A)is shown in greater detail in FIG. 3A. In some embodiments, apparatus110 may be associated with a structure (not shown in FIG. 3A) thatenables easy detaching and reattaching of apparatus 110 to glasses 130.In some embodiments, when apparatus 110 attaches to glasses 130, imagesensor 220 acquires a set aiming direction without the need fordirectional calibration. The set aiming direction of image sensor 220may substantially coincide with the field-of-view of user 100. Forexample, a camera associated with image sensor 220 may be installedwithin apparatus 110 in a predetermined angle in a position facingslightly downwards (e.g., 5-15 degrees from the horizon). Accordingly,the set aiming direction of image sensor 220 may substantially match thefield-of-view of user 100. In some embodiments, wearable apparatus 110may include a vibrating device (not shown). The vibrating device may beused to provide feedback to user 100.

FIG. 3B is an exploded view of the components of the embodimentdiscussed regarding FIG. 3A. Attaching apparatus 110 to glasses 130 maytake place in the following way. Initially, a support 310 may be mountedon glasses 130 using a screw 320, in the side of support 310. Then,apparatus 110 may be clipped on support 310 such that it is aligned withthe field-of-view of user 100. The term “support” includes any device orstructure that enables detaching and reattaching of a device including acamera to a pair of glasses or to another object (e.g., a helmet).Support 310 may be made from plastic (e.g., polycarbonate), metal (e.g.,aluminum), or a combination of plastic and metal (e.g., carbon fibergraphite). Support 310 may be mounted on any kind of glasses (e.g.,eyeglasses, sunglasses, 3D glasses, safety glasses, etc.) using screws,bolts, snaps, or any fastening means used in the art.

In some embodiments, support 310 may include a quick release mechanismfor disengaging and reengaging apparatus 110. For example, support 310and apparatus 110 may include magnetic elements. As an alternativeexample, support 310 may include a male latch member and apparatus 110may include a female receptacle. In other embodiments, support 310 canbe an integral part of a pair of glasses, or sold separately andinstalled by an optometrist. For example, support 310 may be configuredfor mounting on the arms of glasses 130 near the frame front, but beforethe hinge. Alternatively, support 310 may be configured for mounting onthe bridge of glasses 130.

In some embodiments, apparatus 110 may be provided as part of a glassesframe 130, with or without lenses. Additionally, in some embodiments,apparatus 110 may be configured to provide an augmented reality displayprojected onto a lens of glasses 130 (if provided), or alternatively,may include a display for projecting time information, for example,according to the disclosed embodiments. Apparatus 110 may include theadditional display or alternatively, may be in communication with aseparately provided display system that may or may not be attached toglasses 130.

In some embodiments, apparatus 110 may be implemented in a form otherthan wearable glasses, as described above with respect to FIGS. 1B-1D,for example. FIG. 4A is a schematic illustration of an example of anadditional embodiment of apparatus 110 from a first viewpoint. The viewpoint shown in FIG. 4A is from the front of apparatus 110. Apparatus 110includes an image sensor 220, a clip (not shown), a function button (notshown) and a hanging ring 410 for attaching apparatus 110 to, forexample, necklace 140, as shown in FIG. 1B. When apparatus 110 hangs onnecklace 140, the aiming direction of image sensor 220 may not fullycoincide with the field-of-view of user 100, but the aiming directionwould still correlate with the field-of-view of user 100.

FIG. 4B is a schematic illustration of the example of a secondembodiment of apparatus 110, from a second view point. The viewpointshown in FIG. 4B is from a side orientation of apparatus 110. Inaddition to hanging ring 410, as shown in FIG. 4B, apparatus 110 mayfurther include a clip 420. User 100 can use clip 420 to attachapparatus 110 to a shirt or belt 150, as illustrated in FIG. 1C. Clip420 may provide an easy mechanism for disengaging and reengagingapparatus 110 from different articles of clothing. In other embodiments,apparatus 110 may include a female receptacle for connecting with a malelatch of a car mount or universal stand.

In some embodiments, apparatus 110 includes a function button 430 forenabling user 100 to provide input to apparatus 110. Function button 430may accept different types of tactile input (e.g., a tap, a click, adouble-click, a long press, a right-to-left slide, a left-to-rightslide). In some embodiments, each type of input may be associated with adifferent action. For example, a tap may be associated with the functionof taking a picture, while a right-to-left slide may be associated withthe function of recording a video.

The example embodiments discussed above with respect to FIGS. 3A, 3B,4A, and 4B are not limiting. In some embodiments, apparatus 110 may beimplemented in any suitable configuration for performing the disclosedmethods. For example, referring back to FIG. 2, the disclosedembodiments may implement an apparatus 110 according to anyconfiguration including an image sensor 220 and a processor unit 210 toperform image analysis and for communicating with a feedback unit 230.

FIG. 5A is a block diagram illustrating the components of apparatus 110according to an example embodiment. As shown in FIG. 5A, and assimilarly discussed above, apparatus 110 includes an image sensor 220, amemory 550, a processor 210, a feedback outputting unit 230, a wirelesstransceiver 530, and a mobile power source 520. In other embodiments,apparatus 110 may also include buttons, other sensors such as amicrophone, and inertial measurements devices such as accelerometers,gyroscopes, magnetometers, temperature sensors, color sensors, lightsensors, etc. Apparatus 110 may further include a data port 570 and apower connection 510 with suitable interfaces for connecting with anexternal power source or an external device (not shown).

Processor 210, depicted in FIG. 5A, may include any suitable processingdevice. The term “processing device” includes any physical device havingan electric circuit that performs a logic operation on input or inputs.For example, processing device may include one or more integratedcircuits, microchips, microcontrollers, microprocessors, all or part ofa central processing unit (CPU), graphics processing unit (GPU), digitalsignal processor (DSP), field-programmable gate array (FPGA), or othercircuits suitable for executing instructions or performing logicoperations. The instructions executed by the processing device may, forexample, be pre-loaded into a memory integrated with or embedded intothe processing device or may be stored in a separate memory (e.g.,memory 550). Memory 550 may comprise a Random Access Memory (RAM), aRead-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium,a flash memory, other permanent, fixed, or volatile memory, or any othermechanism capable of storing instructions.

Although, in the embodiment illustrated in FIG. 5A, apparatus 110includes one processing device (e.g., processor 210), apparatus 110 mayinclude more than one processing device. Each processing device may havea similar construction, or the processing devices may be of differingconstructions that are electrically connected or disconnected from eachother. For example, the processing devices may be separate circuits orintegrated in a single circuit. When more than one processing device isused, the processing devices may be configured to operate independentlyor collaboratively. The processing devices may be coupled electrically,magnetically, optically, acoustically, mechanically or by other meansthat permit them to interact.

In some embodiments, processor 210 may process a plurality of imagescaptured from the environment of user 100 to determine differentparameters related to capturing subsequent images. For example,processor 210 can determine, based on information derived from capturedimage data, a value for at least one of the following: an imageresolution, a compression ratio, a cropping parameter, frame rate, afocus point, an exposure time, an aperture size, and a lightsensitivity. The determined value may be used in capturing at least onesubsequent image. Additionally, processor 210 can detect imagesincluding at least one hand-related trigger in the environment of theuser and perform an action and or provide an output of information to auser via feedback outputting unit 230.

In another embodiment, processor 210 can change the aiming direction ofimage sensor 220. For example, when apparatus 110 is attached with clip420, the aiming direction of image sensor 220 may not coincide with thefield-of-view of user 100. Processor 210 may recognize certainsituations from the analyzed image data and adjust the aiming directionof image sensor 220 to capture relevant image data. For example, in oneembodiment, processor 210 may detect an interaction with anotherindividual and sense that the individual is not fully in view, becauseimage sensor 220 is tilted down. Responsive thereto, processor 210 mayadjust the aiming direction of image sensor 220 to capture image data ofthe individual. Other scenarios are also contemplated where processor210 may recognize the need to adjust an aiming direction of image sensor220.

In some embodiments, processor 210 may communicate data to feedbackoutputting unit 230, which may include any device configured to provideinformation to a user 100. Feedback outputting unit 230 may be providedas part of apparatus 110 (as shown) or may be provided external toapparatus 110 and communicatively coupled thereto. Feedback-outputtingunit 230 may be configured to output visual or nonvisual feedback basedon signals received from processor 210, such as when processor 210recognizes a hand-related trigger in the analyzed image data.

The term “feedback” refers to any output or information provided inresponse to processing at least one image in an environment. In someembodiments, as similarly described above, feedback may include anaudible or visible indication of time information, detected text ornumerals, the value of currency, a branded product, a person's identity,the identity of a landmark or other environmental situation or conditionincluding the street names at an intersection or the color of a trafficlight, etc., as well as other information associated with each of these.For example, in some embodiments, feedback may include additionalinformation regarding the amount of currency still needed to complete atransaction, information regarding the identified person, historicalinformation or times and prices of admission, etc. of a detectedlandmark, etc. In some embodiments, feedback may include an audibletone, a tactile response, and/or information previously recorded by user100. Feedback-outputting unit 230 may comprise appropriate componentsfor outputting acoustical and tactile feedback. For example,feedback-outputting unit 230 may comprise audio headphones, a vibratingdevice, a hearing aid type device, a speaker, a bone conductionheadphone, interfaces that provide tactile cues, vibrotactilestimulators, etc. In some embodiments, processor 210 may communicatesignals with an external feedback outputting unit 230 via a wirelesstransceiver 530, a wired connection, or some other communicationinterface. In some embodiments, feedback outputting unit 230 may alsoinclude any suitable display device for visually displaying informationto user 100.

As shown in FIG. 5A, apparatus 110 includes memory 550. Memory 550 mayinclude one or more sets of instructions accessible to processor 210 toperform the disclosed methods, including instructions for recognizing ahand-related trigger in the image data. In some embodiments memory 550may store image data (e.g., images, videos) captured from theenvironment of user 100. In addition, memory 550 may store informationspecific to user 100, such as image representations of knownindividuals, favorite products, personal items, and calendar orappointment information, etc. In some embodiments, processor 210 maydetermine, for example, which type of image data to store based onavailable storage space in memory 550. In another embodiment, processor210 may extract information from the image data stored in memory 550.

As further shown in FIG. 5A, apparatus 110 includes mobile power source520. The term “mobile power source” includes any device capable ofproviding electrical power, which can be easily carried by hand (e.g.,mobile power source 520 may weigh less than a pound). The mobility ofthe power source enables user 100 to use apparatus 110 in a variety ofsituations. In some embodiments, mobile power source 520 may include oneor more batteries (e.g., nickel-cadmium batteries, nickel-metal hydridebatteries, and lithium-ion batteries) or any other type of electricalpower supply. In other embodiments, mobile power source 520 may berechargeable and contained within a casing that holds apparatus 110. Inyet other embodiments, mobile power source 520 may include one or moreenergy harvesting devices for converting ambient energy into electricalenergy (e.g., portable solar power units, human vibration units, etc.).

Mobile power source 520 may power one or more wireless transceivers(e.g., wireless transceiver 530 in FIG. 5A). The term “wirelesstransceiver” refers to any device configured to exchange transmissionsover an air interface by use of radio frequency, infrared frequency,magnetic field, or electric field. Wireless transceiver 530 may use anyknown standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®,Bluetooth Smart, 802.15.4. or ZigBee). In some embodiments, wirelesstransceiver 530 may transmit data (e.g., raw image data, processed imagedata, extracted information) from apparatus 110 to computing device 120and/or server 250. Wireless transceiver 530 may also receive data fromcomputing device 120 and/or server 250. In other embodiments, wirelesstransceiver 530 may transmit data and instructions to an externalfeedback outputting unit 230.

FIG. 5B is a block diagram illustrating the components of apparatus 110according to another example embodiment. In some embodiments, apparatus110 includes a first image sensor 220 a, a second image sensor 220 b, amemory 550, a first processor 210 a, a second processor 210 b, afeedback outputting unit 230, a wireless transceiver 530, a mobile powersource 520, and a power connector 510. In the arrangement shown in FIG.5B, each of the image sensors may provide images in a different imageresolution, or face a different direction. Alternatively, each imagesensor may be associated with a different camera (e.g., a wide anglecamera, a narrow angle camera, an IR camera, etc.). In some embodiments,apparatus 110 can select which image sensor to use based on variousfactors. For example, processor 210 a may determine, based on availablestorage space in memory 550, to capture subsequent images in a certainresolution.

Apparatus 110 may operate in a first processing-mode and in a secondprocessing-mode, such that the first processing-mode may consume lesspower than the second processing-mode. For example, in the firstprocessing-mode, apparatus 110 may capture images and process thecaptured images to make real-time decisions based on an identifyinghand-related trigger, for example. In the second processing-mode,apparatus 110 may extract information from stored images in memory 550and delete images from memory 550. In some embodiments, mobile powersource 520 may provide more than fifteen hours of processing in thefirst processing-mode and about three hours of processing in the secondprocessing-mode. Accordingly, different processing-modes may allowmobile power source 520 to produce sufficient power for poweringapparatus 110 for various time periods (e.g., more than two hours, morethan four hours, more than ten hours, etc.).

In some embodiments, apparatus 110 may use first processor 210 a in thefirst processing-mode when powered by mobile power source 520, andsecond processor 210 b in the second processing-mode when powered byexternal power source 580 that is connectable via power connector 510.In other embodiments, apparatus 110 may determine, based on predefinedconditions, which processors or which processing modes to use. Apparatus110 may operate in the second processing-mode even when apparatus 110 isnot powered by external power source 580. For example, apparatus 110 maydetermine that it should operate in the second processing-mode whenapparatus 110 is not powered by external power source 580, if theavailable storage space in memory 550 for storing new image data islower than a predefined threshold.

Although one wireless transceiver is depicted in FIG. 5B, apparatus 110may include more than one wireless transceiver (e.g., two wirelesstransceivers). In an arrangement with more than one wirelesstransceiver, each of the wireless transceivers may use a differentstandard to transmit and/or receive data. In some embodiments, a firstwireless transceiver may communicate with server 250 or computing device120 using a cellular standard (e.g., LTE or GSM), and a second wirelesstransceiver may communicate with server 250 or computing device 120using a short-range standard (e.g., Wi-Fi or Bluetooth®). In someembodiments, apparatus 110 may use the first wireless transceiver whenthe wearable apparatus is powered by a mobile power source included inthe wearable apparatus, and use the second wireless transceiver when thewearable apparatus is powered by an external power source.

FIG. 5C is a block diagram illustrating the components of apparatus 110according to another example embodiment including computing device 120.In this embodiment, apparatus 110 includes an image sensor 220, a memory550 a, a first processor 210, a feedback-outputting unit 230, a wirelesstransceiver 530 a, a mobile power source 520, and a power connector 510.As further shown in FIG. 5C, computing device 120 includes a processor540, a feedback-outputting unit 545, a memory 550 b, a wirelesstransceiver 530 b, and a display 260. One example of computing device120 is a smartphone or tablet having a dedicated application installedtherein. In other embodiments, computing device 120 may include anyconfiguration such as an on-board automobile computing system, a PC, alaptop, and any other system consistent with the disclosed embodiments.In this example, user 100 may view feedback output in response toidentification of a hand-related trigger on display 260. Additionally,user 100 may view other data (e.g., images, video clips, objectinformation, schedule information, extracted information, etc.) ondisplay 260. In addition, user 100 may communicate with server 250 viacomputing device 120.

In some embodiments, processor 210 and processor 540 are configured toextract information from captured image data. The term “extractinginformation” includes any process by which information associated withobjects, individuals, locations, events, etc., is identified in thecaptured image data by any means known to those of ordinary skill in theart. In some embodiments, apparatus 110 may use the extractedinformation to send feedback or other real-time indications to feedbackoutputting unit 230 or to computing device 120. In some embodiments,processor 210 may identify in the image data the individual standing infront of user 100, and send computing device 120 the name of theindividual and the last time user 100 met the individual. In anotherembodiment, processor 210 may identify in the image data, one or morevisible triggers, including a hand-related trigger, and determinewhether the trigger is associated with a person other than the user ofthe wearable apparatus to selectively determine whether to perform anaction associated with the trigger. One such action may be to provide afeedback to user 100 via feedback-outputting unit 230 provided as partof (or in communication with) apparatus 110 or via a feedback unit 545provided as part of computing device 120. For example,feedback-outputting unit 545 may be in communication with display 260 tocause the display 260 to visibly output information. In someembodiments, processor 210 may identify in the image data a hand-relatedtrigger and send computing device 120 an indication of the trigger.Processor 540 may then process the received trigger information andprovide an output via feedback outputting unit 545 or display 260 basedon the hand-related trigger. In other embodiments, processor 540 maydetermine a hand-related trigger and provide suitable feedback similarto the above, based on image data received from apparatus 110. In someembodiments, processor 540 may provide instructions or otherinformation, such as environmental information to apparatus 110 based onan identified hand-related trigger.

In some embodiments, processor 210 may identify other environmentalinformation in the analyzed images, such as an individual standing infront user 100, and send computing device 120 information related to theanalyzed information such as the name of the individual and the lasttime user 100 met the individual. In a different embodiment, processor540 may extract statistical information from captured image data andforward the statistical information to server 250. For example, certaininformation regarding the types of items a user purchases, or thefrequency a user patronizes a particular merchant, etc. may bedetermined by processor 540. Based on this information, server 250 maysend computing device 120 coupons and discounts associated with theuser's preferences.

When apparatus 110 is connected or wirelessly connected to computingdevice 120, apparatus 110 may transmit at least part of the image datastored in memory 550 a for storage in memory 550 b. In some embodiments,after computing device 120 confirms that transferring the part of imagedata was successful, processor 540 may delete the part of the imagedata. The term “delete” means that the image is marked as ‘deleted’ andother image data may be stored instead of it, but docs not necessarilymean that the image data was physically removed from the memory.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the disclosed embodiments. Not all components are essential for theoperation of apparatus 110. Any component may be located in anyappropriate apparatus and the components may be rearranged into avariety of configurations while providing the functionality of thedisclosed embodiments. For example, in some embodiments, apparatus 110may include a camera, a processor, and a wireless transceiver forsending data to another device. Therefore, the foregoing configurationsare examples and, regardless of the configurations discussed above,apparatus 110 can capture, store, and or process images.

Further, the foregoing and following description refers to storingand/or processing images or image data. In the embodiments disclosedherein, the stored and or processed images or image data may comprise arepresentation of one or more images captured by image sensor 220. Asthe term is used herein, a “representation” of an image (or image data)may include an entire image or a portion of an image. A representationof an image (or image data) may have the same resolution or a lowerresolution as the image (or image data), andor a representation of animage (or image data) may be altered in some respect (e.g., becompressed, have a lower resolution, have one or more colors that arealtered, etc.).

For example, apparatus 110 may capture an image and store arepresentation of the image that is compressed as a JPG file. As anotherexample, apparatus 110 may capture an image in color, but store ablack-and-white representation of the color image. As yet anotherexample, apparatus 110 may capture an image and store a differentrepresentation of the image (e.g., a portion of the image). For example,apparatus 110 may store a portion of an image that includes a face of aperson who appears in the image, but that does not substantially includethe environment surrounding the person. Similarly, apparatus 110 may,for example, store a portion of an image that includes a product thatappears in the image, but does not substantially include the environmentsurrounding the product. As yet another example, apparatus 110 may storea representation of an image at a reduced resolution (i.e., at aresolution that is of a lower value than that of the captured image).Storing representations of images may allow apparatus 110 to savestorage space in memory 550. Furthermore, processing representations ofimages may allow apparatus 110 to improve processing efficiency and orhelp to preserve battery life.

In addition to the above, in some embodiments, any one of apparatus 110or computing device 120, via processor 210 or 540, may further processthe captured image data to provide additional functionality to recognizeobjects andor gestures and/or other information in the captured imagedata. In some embodiments, actions may be taken based on the identifiedobjects, gestures, or other information. In some embodiments, processor210 or 540 may identity in the image data, one or more visible triggers,including a hand-related trigger, and determine whether the trigger isassociated with a person other than the user to determine whether toperform an action associated with the trigger.

Some embodiments of the present disclosure may include an apparatussecurable to an article of clothing, of a user. Such an apparatus mayinclude two portions, connectable by a connector. A capturing unit maybe designed to be worn on the outside of a user's clothing, and mayinclude an image sensor for capturing images of a user's environment.The capturing unit may be connected to or connectable to a power unit,which may be configured to house a power source and a processing device.The capturing unit may be a small device including a camera or otherdevice for capturing images. The capturing unit may be designed to beinconspicuous and unobtrusive, and may be configured to communicate witha power unit concealed by a user's clothing. The power unit may includebulkier aspects of the system, such as transceiver antennas, at leastone battery, a processing device, etc. In some embodiments,communication between the capturing unit and the power unit may beprovided by a data cable included in the connector, while in otherembodiments, communication may be wirelessly achieved between thecapturing unit and the power unit. Some embodiments may permitalteration of the orientation of an image sensor of the capture unit,for example to better capture images of interest.

An apparatus consistent with embodiments of the present disclosure mayalso include at least one vibrating device. For example, the vibratingdevice may be integrated with the wearable apparatus and/or may behoused in a unit separate from and in communication with the wearableapparatus (e.g., a smartphone, a smartwatch, a tablet, or the like).

FIG. 6 illustrates an exemplary embodiment of a memory containingsoftware modules consistent with the present disclosure. Included inmemory 550 are orientation identification module 601, orientationadjustment module 602, and motion tracking module 603. Modules 601, 602,603 may contain software instructions for execution by at least oneprocessing device, e.g., processor 210, included with a wearableapparatus. Orientation identification module 601, orientation adjustmentmodule 602, and motion tracking module 603 may cooperate to provideorientation adjustment for a capturing unit incorporated into wirelessapparatus 110.

FIG. 7 illustrates an exemplary capturing unit 710 including anorientation adjustment unit 705. Orientation adjustment unit 705 may beconfigured to permit the adjustment of image sensor 220. As illustratedin FIG. 7, orientation adjustment unit 705 may include an eye-ball typeadjustment mechanism. In alternative embodiments, orientation adjustmentunit 705 may include gimbals, adjustable stalks, pivotable mounts, andany other suitable unit for adjusting an orientation of image sensor220.

Image sensor 220 may be configured to be movable with the head of user100 in such a manner that an aiming direction of image sensor 220substantially coincides with a field of view of user 100. For example,as described above, a camera associated with image sensor 220 may beinstalled within capturing unit 710 at a predetermined angle in aposition facing slightly upwards or downwards, depending on an intendedlocation of capturing unit 710. Accordingly, the set aiming direction ofimage sensor 220 may match the field-of-view of user 100. In someembodiments, processor 210 may change the orientation of image sensor220 using image data provided from image sensor 220. For example,processor 210 may recognize that a user is reading a book and determinethat the aiming direction of image sensor 220 is offset from the text.That is, because the words in the beginning of each line of text are notfully in view, processor 210 may determine that image sensor 220 istilted in the wrong direction. Responsive thereto, processor 210 mayadjust the aiming direction of image sensor 220.

Orientation identification module 601 may be configured to identify anorientation of an image sensor 220 of capturing unit 710. An orientationof an image sensor 220 may be identified, for example, by analysis ofimages captured by image sensor 220 of capturing unit 710, by tilt orattitude sensing devices within capturing unit 710, and by measuring arelative direction of orientation adjustment unit 705 with respect tothe remainder of capturing unit 710.

Orientation adjustment module 602 may be configured to adjust anorientation of image sensor 220 of capturing unit 710. As discussedabove, image sensor 220 may be mounted on an orientation adjustment unit705 configured for movement. Orientation adjustment unit 705 may beconfigured for rotational and/or lateral movement in response tocommands from orientation adjustment module 602. In some embodimentsorientation adjustment unit 705 may be adjust an orientation of imagesensor 220 via motors, electromagnets, permanent magnets, and/or anysuitable combination thereof.

In some embodiments, monitoring module 603 may be provided forcontinuous monitoring. Such continuous monitoring may include tracking amovement of at least a portion, an object included in one or more imagescaptured by the image sensor. For example, in one embodiment, apparatus110 may track an object as long as the object remains substantiallywithin the field-of-view of image sensor 220. In additional embodiments,monitoring module 603 may engage orientation adjustment module 602 toinstruct orientation adjustment unit 705 to continually orient imagesensor 220 towards an object of interest. For example, in oneembodiment, monitoring module 603 may cause image sensor 220 to adjustan orientation to ensure that a certain designated object, for example,the face of a particular person, remains within the field-of view ofimage sensor 220, even as that designated object moves about. In anotherembodiment, monitoring module 603 may continuously monitor an area ofinterest included in one or more images captured by the image sensor.For example, a user may be occupied by a certain task, for example,typing on a laptop, while image sensor 220 remains oriented in aparticular direction and continuously monitors a portion of each imagefrom a series of images to detect a trigger or other event. For example,image sensor 210 may be oriented towards a piece of laboratory equipmentand monitoring module 603 may be configured to monitor a status light onthe laboratory equipment for a change in status, while the user'sattention is otherwise occupied.

In some embodiments consistent with the present disclosure, capturingunit 710 may include a plurality of image sensors 220. The plurality ofimage sensors 220 may each be configured to capture different imagedata. For example, when a plurality of image sensors 220 are provided,the image sensors 220 may capture images having different resolutions,may capture wider or narrower fields of view, and may have differentlevels of magnification. Image sensors 220 may be provided with varyinglenses to permit these different configurations. In some embodiments, aplurality of image sensors 220 may include image sensors 220 havingdifferent orientations. Thus, each of the plurality of image sensors 220may be pointed in a different direction to capture different images. Thefields of view of image sensors 220 may be overlapping in someembodiments. The plurality of image sensors 220 may each be configuredfor orientation adjustment, for example, by being paired with an imageadjustment unit 705. In some embodiments, monitoring module 603, oranother module associated with memory 550, may be configured toindividually adjust the orientations of the plurality of image sensors220 as well as to turn each of the plurality of image sensors 220 on oroff as may be required. In some embodiments, monitoring an object orperson captured by an image sensor 220 may include tracking movement ofthe object across the fields of view of the plurality of image sensors220.

Embodiments consistent with the present disclosure may includeconnectors configured to connect a capturing unit and a power unit of awearable apparatus. Capturing units consistent with the presentdisclosure may include at least one image sensor configured to captureimages of an environment of a user. Power units consistent with thepresent disclosure may be configured to house a power source and/or atleast one processing device. Connectors consistent with the presentdisclosure may be configured to connect the capturing unit and the powerunit, and may be configured to secure the apparatus to an article ofclothing such that the capturing unit is positioned over an outersurface of the article of clothing and the power unit is positionedunder an inner surface of the article of clothing. Exemplary embodimentsof capturing units, connectors, and power units consistent with thedisclosure are discussed in further detail with respect to FIGS. 8-14.

FIG. 8 is a schematic illustration of an embodiment of wearableapparatus 110 securable to an article of clothing consistent with thepresent disclosure. As illustrated in FIG. 8, capturing unit 710 andpower unit 720 may be connected by a connector 730 such that capturingunit 710 is positioned on one side of an article of clothing 750 andpower unit 720 is positioned on the opposite side of the clothing 750.In some embodiments, capturing unit 710 may be positioned over an outersurface of the article of clothing 750 and power unit 720 may be locatedunder an inner surface of the article of clothing 750. The power unit720 may be configured to be placed against the skin of a user.

Capturing unit 710 may include an image sensor 220 and an orientationadjustment unit 705 (as illustrated in FIG. 7). Power unit 720 mayinclude mobile power source 520 and processor 210. Power unit 720 mayfurther include any combination of elements previously discussed thatmay be a part of wearable apparatus 110, including, but not limited to,wireless transceiver 530, feedback outputting unit 230, memory 550, anddata port 570.

Connector 730 may include a clip 715 or other mechanical connectiondesigned to clip or attach capturing unit 710 and power unit 720 to anarticle of clothing 750 as illustrated in FIG. 8. As illustrated, clip715 may connect to each of capturing unit 710 and power unit 720 at aperimeter thereof, and may wrap around an edge of the article ofclothing 750 to affix the capturing unit 710 and power unit 720 inplace. Connector 730 may further include a power cable 760 and a datacable 770. Power cable 760 may be capable of conveying power from mobilepower source 520 to image sensor 220 of capturing unit 710. Power cable760 may also be configured to provide power to any other elements ofcapturing unit 710, e.g., orientation adjustment unit 705. Data cable770 may be capable of conveying captured image data from image sensor220 in capturing unit 710 to processor 800 in the power unit 720. Datacable 770 may be further capable of conveying additional data betweencapturing unit 710 and processor 800, e.g., control instructions fororientation adjustment unit 705.

FIG. 9 is a schematic illustration of a user 100 wearing a wearableapparatus 110 consistent with an embodiment of the present disclosure.As illustrated in FIG. 9, capturing unit 710 is located on an exteriorsurface of the clothing 750 of user 100. Capturing unit 710 is connectedto power unit 720 (not seen in this illustration) via connector 730,which wraps around an edge of clothing 750.

In some embodiments, connector 730 may include a flexible printedcircuit board (PCB). FIG. 10 illustrates an exemplary embodiment whereinconnector 730 includes a flexible printed circuit board 765. Flexibleprinted circuit board 765 may include data connections and powerconnections between capturing unit 710 and power unit 720. Thus, in someembodiments, flexible printed circuit board 765 may serve to replacepower cable 760 and data cable 770. In alternative embodiments, flexibleprinted circuit board 765 may be included in addition to at least one ofpower cable 760 and data cable 770. In various embodiments discussedherein, flexible printed circuit board 765 may be substituted for, orincluded in addition to, power cable 760 and data cable 770.

FIG. 11 is a schematic illustration of another embodiment of a wearableapparatus securable to an article of clothing consistent with thepresent disclosure. As illustrated in FIG. 11, connector 730 may becentrally located with respect to capturing unit 710 and power unit 720.Central location of connector 730 may facilitate affixing apparatus 110to clothing 750 through a hole in clothing 750 such as, for example, abutton-hole in an existing article of clothing 750 or a specialty holein an article of clothing 750 designed to accommodate wearable apparatus110.

FIG. 12 is a schematic illustration of still another embodiment ofwearable apparatus 110 securable to an article of clothing. Asillustrated in FIG. 12, connector 730 may include a first magnet 731 anda second magnet 732. First magnet 731 and second magnet 732 may securecapturing unit 710 to power unit 720 with the article of clothingpositioned between first magnet 731 and second magnet 732. Inembodiments including first magnet 731 and second magnet 732, powercable 760 and data cable 770 may also be included. In these embodiments,power cable 760 and data cable 770 may be of any length, and may providea flexible power and data connection between capturing unit 710 andpower unit 720. Embodiments including first magnet 731 and second magnet732 may further include a flexible PCB 765 connection in addition to orinstead of power cable 760 and/or data cable 770. In some embodiments,first magnet 731 or second magnet 732 may be replaced by an objectcomprising a metal material.

FIG. 13 is a schematic illustration of yet another embodiment of awearable apparatus 110 securable to an article of clothing. FIG. 13illustrates an embodiment wherein power and data may be wirelesslytransferred between capturing unit 710 and power unit 720. Asillustrated in FIG. 13, first magnet 731 and second magnet 732 may beprovided as connector 730 to secure capturing unit 710 and power unit720 to an article of clothing 750. Power and/or data may be transferredbetween capturing unit 710 and power unit 720 via any suitable wirelesstechnology, for example, magnetic and/or capacitive coupling, near fieldcommunication technologies, radiofrequency transfer, and any otherwireless technology suitable for transferring data and/or power acrossshort distances.

FIG. 14 illustrates still another embodiment of wearable apparatus 110securable to an article of clothing 750 of a user. As illustrated inFIG. 14, connector 730 may include features designed for a contact fit.For example, capturing unit 710 may include a ring 733 with a hollowcenter having a diameter slightly larger than a disk-shaped protrusion734 located on power unit 720. When pressed together with fabric of anarticle of clothing 750 between them, disk-shaped protrusion 734 may fittightly inside ring 733, securing capturing unit 710 to power unit 720.FIG. 14 illustrates an embodiment that does not include any cabling orother physical connection between capturing unit 710 and power unit 720.In this embodiment, capturing unit 710 and power unit 720 may transferpower and data wirelessly. In alternative embodiments, capturing unit710 and power unit 720 may transfer power and data via at least one ofcable 760, data cable 770, and flexible printed circuit board 765.

FIG. 15 illustrates another aspect of power unit 720 consistent withembodiments described herein. Power unit 720 may be configured to bepositioned directly against the user's skin. To facilitate suchpositioning, power unit 720 may further include at least one surfacecoated with a biocompatible material 740. Biocompatible materials 740may include materials that will not negatively react with the skin ofthe user when worn against the skin for extended periods of time. Suchmaterials may include, for example, silicone. PTFE, kapton, polyimide,titanium, nitinol, platinum, and others. Also as illustrated in FIG. 15,power unit 720 may be sized such that an inner volume of the power unitis substantially filled by mobile power source 520. That is, in someembodiments, the inner volume of power unit 720 may be such that thevolume does not accommodate any additional components except for mobilepower source 520. In some embodiments, mobile power source 520 may takeadvantage of its close proximity to the skin of user's skin. Forexample, mobile power source 520 may use the Peltier effect to producepower andor charge the power source.

In further embodiments, an apparatus securable to an article of clothingmay further include protective circuitry associated with power source520 housed in in power unit 720. FIG. 16 illustrates an exemplaryembodiment including protective circuitry 775. As illustrated in FIG.16, protective circuitry 775 may be located remotely with respect topower unit 720. In alternative embodiments, protective circuitry 775 mayalso be located in capturing unit 710, on flexible printed circuit board765, or in power unit 720.

Protective circuitry 775 may be configured to protect image sensor 220and/or other elements of capturing unit 710 from potentially dangerouscurrents andor voltages produced by mobile power source 520. Protectivecircuitry 775 may include passive components such as capacitors,resistors, diodes, inductors, etc., to provide protection to elements ofcapturing unit 710. In some embodiments, protective circuitry 775 mayalso include active components, such as transistors, to provideprotection to elements of capturing unit 710. For example, in someembodiments, protective circuitry 775 may comprise one or more resistorsserving as fuses. Each fuse may comprise a wire or strip that melts(thereby braking a connection between circuitry of image capturing unit710 and circuitry of power unit 720) when current flowing through thefuse exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps,1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.). Any or all of thepreviously described embodiments may incorporate protective circuitry775.

In some embodiments, the wearable apparatus may transmit data to acomputing device (e.g., a smartphone, tablet, watch, computer, etc.)over one or more networks via any known wireless standard (e.g.,cellular, Wi-Fi, Bluetooth®, etc.), or via near-field capacitivecoupling, other short range wireless techniques, or via a wiredconnection. Similarly, the wearable apparatus may receive data from thecomputing device over one or more networks via any known wirelessstandard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-fieldcapacitive coupling, other short range wireless techniques, or via awired connection. The data transmitted to the wearable apparatus and orreceived by the wireless apparatus may include images, portions ofimages, identifiers related to information appearing in analyzed imagesor associated with analyzed audio, or any other data representing imageand/or audio data. For example, an image may be analyzed and anidentifier related to an activity occurring in the image may betransmitted to the computing device (e.g., the “paired device”). In theembodiments described herein, the wearable apparatus may process imagesand/or audio locally (on board the wearable apparatus) and/or remotely(via a computing device). Further, in the embodiments described herein,the wearable apparatus may transmit data related to the analysis ofimages and/or audio to a computing device for further analysis, display,and or transmission to another device (e.g., a paired device). Further,a paired device may execute one or more applications (apps) to process,display, and or analyze data (e.g., identifiers, text, images, audio,etc.) received from the wearable apparatus.

Some of the disclosed embodiments may involve systems, devices, methods,and software products for determining at least one keyword. For example,at least one keyword may be determined based on data collected byapparatus 110. At least one search query may be determined based on theat least one keyword. The at least one search query may be transmittedto a search engine.

In some embodiments, at least one keyword may be determined based on atleast one or more images captured by image sensor 220. In some cases,the at least one keyword may be selected from a keywords pool stored inmemory. In some cases, optical character recognition (OCR) may beperformed on at least one image captured by image sensor 220, and the atleast one keyword may be determined based on the OCR result. In somecases, at least one image captured by image sensor 220 may be analyzedto recognize: a person, an object, a location, a scene, and so forth.Further, the at least one keyword may be determined based on therecognized person, object, location, scene, etc. For example, the atleast one keyword may comprise: a person's name, an object's name, aplace's name, a date, a sport team's name, a movie's name, a book'sname, and so forth.

In some embodiments, at least one keyword may be determined based on theuser's behavior. The user's behavior may be determined based on ananalysis of the one or more images captured by image sensor 220. In someembodiments, at least one key word may be determined based on activitiesof a user and or other person. The one or more images captured by imagesensor 220 may be analyzed to identify the activities of the user and/orthe other person who appears in one or more images captured by imagesensor 220. In some embodiments, at least one keyword may be determinedbased on at least one or more audio segments captured by apparatus 110.In some embodiments, at least one keyword may be determined based on atleast GPS information associated with the user. In some embodiments, atleast one keyword may be determined based on at least the current timeand or date.

In some embodiments, at least one search query may be determined basedon at least one keyword. In some cases, the at least one search querymay comprise the at least one keyword. In some eases, the at least onesearch query may comprise the at least one keyword and additional keywords provided by the user. In some cases, the at least one search querymay comprise the at least one key word and one or more images, such asimages captured by image sensor 220. In some cases, the at least onesearch query may comprise the at least one keyword and one or more audiosegments, such as audio segments captured by apparatus 110.

In some embodiments, the at least one search query may be transmitted toa search engine. In some embodiments, search results provided by thesearch engine in response to the at least one search query may beprovided to the user. In some embodiments, the at least one search querymay be used to access a database.

For example, in one embodiment, the keywords may include a name of atype of food, such as quinoa, or a brand name of a food product; and thesearch will output information related to desirable quantities ofconsumption, facts about the nutritional profile, and so forth. Inanother example, in one embodiment, the keywords may include a name of arestaurant, and the search will output information related to therestaurant, such as a menu, opening hours, reviews, and so forth. Thename of the restaurant may be obtained using OCR on an image of signage,using GPS information, and so forth. In another example, in oneembodiment, the keywords may include a name of a person, and the searchwill provide information from a social network profile of the person.The name of the person may be obtained using OCR on an image of a nametag attached to the person's shin, using face recognition algorithms,and so forth. In another example, in one embodiment, the keywords mayinclude a name of a book, and the search will output information relatedto the book, such as reviews, sales statistics. information regardingthe author of the hook, and so forth. In another example, in oneembodiment, the keywords may include a name of a movie, and the searchwill output information related to the movie, such as reviews, boxoffice statistics, information regarding the cast of the movie, showtimes, and so forth. In another example, in one embodiment, the keywordsmay include a name of a sport team, and the search will outputinformation related to the sport team, such as statistics, latestresults, future schedule, information regarding the players of the sportteam, and so forth. For example, the name of the sport team may beobtained using audio recognition algorithms.

In some embodiments, wearable apparatus 110 may monitor consumption of auser and provide feedback relating to the consumption. For example,wearable apparatus 110 may automatically monitor the consumption of auser by analyzing images captured from an environment of the user. Theimages may be captured by at least one image capture device configuredto capture a plurality of images from the environment of user 100 ofwearable apparatus 110. For example, the at least one image capturedevice may include image sensor 220 or a video camera or a combinationthereof. It is contemplated that plurality of images may be captured bya plurality of image capture devices. For example, user 100 may wearmore than one wearable apparatus 110 or may wear one wearable apparatus110 and have a second means for capturing images of his or herenvironment. In some embodiments, wearable apparatus 110 may include atleast one processing device. For example, wearable apparatus 110 mayinclude processor 210, which may be configured to monitor consumption ofuser 100. By way of example, processor 210 may be configured to analyzethe images captured by the one or more image capture devices and todetect consumable objects in one or more images. In some embodiments,wearable apparatus 110 may be paired to another device (e.g., asmartphone, tablet, smartwatch, or the like). The other device may beconfigured to perform all or a portion of the method for monitoringconsumption of a user.

FIG. 17 illustrates an exemplary embodiment of a memory 1700 containingmodules consistent with the present disclosure. Memory 1700 may beincluded in wearable apparatus 110 (e.g., as memory 550 a), may beincluded in another device in communication with wearable apparatus 110(e.g., in computing device 120), or may be a stand-alone device. Ananalysis module, such as image analysis module 1701, may be included inmemory 1700. A feedback determination module 1702 and/or a feedbackoutput module 1703 may be included in memory 1700. Each module 1701,1702, and 1703 may contain software instructions for performing all or aportion of a method for monitoring consumption of a user and providingfeedback related thereto. The instructions may be executed by aprocessing device, such as processor 210 and or processor 540, includedin wearable apparatus 110 or computing device 120, respectively.

Image analysis module 1701 may include instructions for identifying atleast one consumption indicator from one or more images. A consumptionindicator may be identified, for example, by analyzing one or moreimages for a known indicator of consumption. For example, a consumptionindicator may be an object such as a spoon, fork, cup, glass, cigarette,cigar, a piece of food, a snack, a menu, a pill bottle, or the like. Theconsumption indicator may also be associated with gestures or movementof a user that are indicative of consumption. For example, theconsumption indicator may include movement of a hand, utensil, cup, orother object to and from the mouth, or movement of the jaws in a chewingpattern, or other movement associated with consumption. The consumptionindicator may also include audible indicators, such as noises associatedwith chewing, swallowing, or the like. Consistent with this disclosure,one or more consumption indicators may be detected by analysis of one ormore images, a video, or a combination thereof. Detection of aconsumption indicator may initiate the automatic monitoring ofconsumption and trigger other actions related to identifying the typeand amount of consumable product consumed and providing feedback basedon the consumption.

In some embodiments, one or more algorithms may be used to detectconsumption. For example, a consumption recognition algorithm may beconfigured to access a database containing known consumables and tocompare captured images with the known consumable to accurately detect aconsumable product and to detect a type of the consumable product. As anexample, image processing module 1701 may include instructions fordetecting a consumption indicator, such as a gesture associated withconsumption, and being analyzing images received from an image capturedevice to detect consumable products. When an object that may be aconsumable product is detected, image analysis module 1701 may includeinstructions for accessing a database, for example, and comparing theobject with known consumables to confirm that the detected object is aconsumable product. The database may be included in the wearableapparatus or accessed over a network (e.g., a remote server). The typeof the consumable product may be determined in substantially the samemanner. For example, image analysis module 1701 may include instructionsfor comparing a captured image with a plurality of known consumableproducts to determine that, for example, the object detected in theimage is a hamburger. Image analysis module 1701 may includeinstructions for comparing the image of the detected hamburger with aplurality of known hamburger types to determine the type of thehamburger. For example, one or more captured images may be compared withknown images of hamburgers. The type of the hamburger may be, forexample, a brand name associated with the hamburger or a break down ofthe components on the hamburger. Image analysis module 1701 may includeinstructions for detecting a consumable product and may determine a typeof the detected consumable product by any means disclosed herein.Consistent with this disclosure, image analysis module 1701 may includeinstructions for performing other operations for determining an amountof a consumable product that a person has consumed, the amount ofcalories or nutrients consumed, or the like.

Feedback determination module 1702 may include instructions forprocessing the data collected and generated by the operations of imageanalysis module 1701 and determining a feedback for the user. Forexample, feedback determination module 1702 may include instructions fordetermining a feedback based on the type indicator of the detectedconsumable product and the estimated amount of the consumable productconsumed by the user. In some embodiments, the estimated amount of theconsumable product consumed by the user may include an amount ofcalories or nutrients consumed by a user, or a quantity of a particularitem (e.g. three cookies). For example, nutrition information for adetected type of consumable may be accessed through a network or from adatabase and used to determine the amount of fat, protein,carbohydrates, sodium, cholesterol, etc. consumed by the user. It isalso contemplated that feedback determination module 1702 may includeinstructions for accessing user settings, preferences, or goals anddetermine feedback based thereon. For example, a user may indicate inuser preferences a maximum number of calories he or she intends toconsume during a meal, day, week, month, or other time frame andfeedback determination module 1702 may include instructions fordetermining how a consumed amount of calories compares to the maximumand determine a feedback consistent with the comparison. The feedbackdetermined by the operations of feedback determination module 1702 maybe any consistent with this disclosure.

Feedback output module 1703 may include instructions for providing thefeedback determined by the operations of feedback determination module1702 to an outputting unit. For example, feedback output module 1703 mayprovide the feedback for output to a display, speaker, or vibrationmotor of wearable apparatus 110, a display, speaker, or vibration motorof computing device 120, any other device in communication withprocessing device 1700, or the like. The feedback may be transmitteddirectly for output or stored in a memory for later output, or acombination thereof. For example, the feedback may be stored in amemory, such as memory 550 or 550 b, where it may be aggregated overtime and the aggregated feedback may be output at a later time or usedto determine further feedback or the like. As another example, thefeedback may be transmitted directly to an outputting unit, such asfeedback outputting unit 230 or 545. The feedback may be output by anymeans disclosed herein.

Consistent with this disclosure, wearable apparatus 110 may beconfigured to capture a plurality of images of a users environment. Forexample, wearable apparatus 110 may be worn by user 100 in any mannerdisclosed herein and may capture images of the field of view of user100. In another example, wearable device 110 may be worn by user 100 andmay be configured to focus on an object or area of interest within anenvironment, as described above, and may capture images of the object orarea. For example, image sensor 220 may be focused on a table, plate,hand, utensil, or other object related to consumption and the imagescaptured may include representations of consumable products. Asdiscussed above, the environment may include any area around the user,the field of view of the user, or an area of interest within view ofimage sensor 220 of wearable apparatus 110.

FIG. 18 is an illustrative example of an image 1800 that may be capturedby wearable apparatus 110. Image 1800 includes several objects, bothconsumable and non-consumable, located on a table 1830. Images similarto image 1800 may be captured when, for example, user 100 is wearingwearable apparatus 110 while dinning in a restaurant or anotherlocation. FIG. 19 is another illustrative example of an image 1900 thatmay be captured by wearable apparatus 110. Image 1900 includes arepresentation of an arm 1920 and a hand 1928 belonging to user 100, anda cheeseburger 1902 in hand 1928. Images similar to image 1900 may becaptured by wearable apparatus, for example, as user 100 is bringing anobject, such as cheeseburger 1902, to his or her mouth. FIG. 18 and FIG.19 illustrate examples of the present disclosure. It is understood thatthey are not limiting in any way and the images captured by wearableapparatus 110 may be substantially different from either image 1800 orimage 1900.

The images may be captured in response to detection of a consumptionindicator, as discussed above. For example, wearable apparatus 110 maybe configured to begin capturing images of user's 100 environment inresponse to detection of chewing, swallowing, or other action associatedwith consumption. In some embodiments, wearable apparatus 110 may beconfigured to periodically capture images of a user's environment and befurther configured to capture images of the environment more frequentlyafter detection of a consumption indicator. For example, wearableapparatus 110 may capture images of the environment at a predeterminedinterval (e.g., every second, every five seconds, every ten seconds,every fifteen seconds, etc.) and when a consumption indicator, such as afork, cup, cigarette carton, etc. is detected in one of the images,wearable apparatus 110 may be configured to increase the rate of imagecapture to a predetermined rate (e.g., such that it is capturing imagesevery second, every half-second, every tenth of a second, etc.). Thetime intervals are exemplary only and not limiting. In anotherembodiment, wearable apparatus 110 may be configured to continuouslycapture images or video of the environment and to continuously monitorfor consumable products.

Consistent with this disclosure, the at least one processing device maybe configured to analyze a plurality of images to detect a consumableproduct represented in at least one of the plurality of images. Asdiscussed above, the plurality of images may be captured by wearableapparatus 110, for example by image sensor 220. It is also contemplatedthat the plurality of images may be captured by another device (forexample, a second wearable apparatus 110) and transmitted to wearableapparatus 110. It is further contemplated that the plurality of imagesmay be captured by one or more wearable apparatus 100 and the images ordata relating thereto may be transmitted to computing device 120 oranother device for processing (e.g., a remote server accessible over oneor more networks). In some embodiments, the plurality of images may bereceived by wearable apparatus 110 or computing device 120 and analyzedto detect consumable products.

In some embodiments, the at least one processing device may use one ormore image analysis algorithms or image analysis techniques to detectconsumable products. For example, detection of a consumable product maybe accomplished by appearance-based algorithms, template-matching basedalgorithms, skeletal based algorithms, color-recognition algorithms,machine-learning based algorithms, neural-network based algorithms,vector analysis algorithms, and so forth. In another example, detectionof a consumable product may be accomplished using image enhancement,edge detection, data extraction, and so forth. For example, wearableapparatus may compare the representation of objects in captured imagewith images of objects of known types. The images used for comparisonmay be stored on a memory (e.g., memory 550 or 550 b) or may be storedin and accessed from a database (e.g., a local database local to thewearable apparatus and/or a remote database accessible over one or morenetworks). The images used for comparison may include additionalinformation, such as an indicator of types of consumable productscommonly associated with those objects. This information may aid indetermining a type of a detected consumable product, as described below.

A consumable product may be detected directly or may be detected basedon its relation to an object associated with consumption. For example, aconsumable product may be detected by first detecting a plate or cupresting on a table and then detecting an object on the plate or in thecup. The object associated with consumption may be identified bycomparison of the object with images of objects of a known type. Forexample, when analyzing image 1800, wearable apparatus 110 may detectobjects 1810 and 1814. By comparing the general shape, size, and visualappearance of objects 1810 and 1814 with images of known objects,wearable apparatus 110 may determine that object 1810 is a glass andobject 1814 is a wine glass. Based on the comparison, wearable apparatus110 may detect that liquid 1812 and 1816 are consumable products. Asanother example, wearable apparatus may detect object 1824 in image 1800and determine, by substantially the same process, that object 1824 is acandle. Based on this determination, wearable apparatus 110 maydetermine that the contents of candle 1824 are not consumable products.

In another example, the at least one processing device may directlyidentify a consumable object. For example, wearable apparatus 110 maydirectly detect a consumable product by, for example, identifying anobject having the same shape or appearance as a known consumableproduct. For example, in image 1800, wearable apparatus 110 may detectobject 1822 and determine (e.g., based on its distinctive shape, acomparison with known objects, etc.) that object 1822 is a consumableproduct. It is also contemplated that wearable apparatus 110 maydetermine that object 1822 is a dessert or, more specifically, a pieceof cake. On the other hand, wearable apparatus 110 may determine thatobject 1820 is a plate and is not consumable.

In some embodiments, the at least one processing device may beconfigured to detect a plurality of consumable products anddifferentiate between those products. For example, while analyzing image1800, wearable apparatus 100 may identify object 1802 and properlyclassify it as a plate. Wearable apparatus 110 may then detect theobjects located on plate 1802 and determine that they are consumableproducts. Wearable apparatus 110 may identify three objects 1804, 1808,and 1806 on plate 1802. The objects 1804, 1808, and 1806 may beidentified based on an analysis of the outline of each object, of theshapes related with each object, of the color associated with eachobject, and so forth. For example, wearable apparatus 110 may detect afirst object 1804 and differentiate it from the other objects on plate1802 based on the relative shape, size, texture, or other visualappearance of object 1804. The same may be true for objects 1808 and1806. Wearable apparatus 110 may further be configured to recognize thatsome consumable products may be comprised of a plurality of objects. Forexample, wearable apparatus 110 may be able to recognize that object1808 is a single mass and that object 1806 is comprised of severalcomponents. The components of object 1806 may be identified as belongingto object 1806 based on, for example, each component being more similarto the other components of object 1806 than they are to other objects inthe vicinity, such as object 1804 or object 1808.

In some embodiments, the at least one processing device may determinethat an object is a consumable product based on its association with aconsumption indicator. For example, as described above, certainmovements of user 100, such as bringing a hand or utensil near the mouthrepeatedly, are indicators of consumption. When analyzing image 1900,wearable apparatus 110 may determine that hand 1928 is approaching user100's mouth. This may be based on, for example, a determination thathand 1928 is closer to the mouth than typical or that it is closer tothe mouth than in an image captured just prior to image 1900. Based onthe detection of the consumption indicator, wearable apparatus 110 maydetermine that an object in hand 1928 is a consumable product. Wearableapparatus 110 may then analyze image 1900 to detect the outline of hand1928, including fingers 1922, thumb 1924, or palm 1926 and detect otherobjects not related to hand 1928. The other objects, here object 1902,may be determined to be a consumable product. It is also contemplatedthat wearable apparatus may detect object 1902 directly, as describedabove.

External information, such as location coordinates, scheduleinformation, historical information, textual cues, audio cues, or thelike, may aid in detection of a consumable product. For example,wearable apparatus 110 may receive location information relating towhere user 100 is located in space. If user 100 is in a restaurant orother location associated with consumption, wearable apparatus 110 maybe configured to assume that at least some objects detected at thatlocation are consumable products. In another example, wearable apparatus110 may access, for example, schedule information relating to areservation user 100 has at a restaurant and may be configured todetermine that at least some objects detected during the time of thereservation are consumable products. In another example, wearableapparatus 110 may access historical data relating to previous instanceswherein a consumable product was detected, determine a pattern relatedthereto, and determine that objects detected consistent with thatpattern are consumable objects. For example, if user 100 has a cigarettebreak at a similar time every day, wearable apparatus 110 may detect theconsumption of a cigarette a plurality of times and determine a patternrelating to where and when the cigarette has been consumed. On laterdates, wearable apparatus 110 may use that information to aid indetecting a cigarette consumed under circumstances with conditionssimilar to the determined pattern.

Consistent with this disclosure, the at least one processing device maybe configured to, based on the detection of the consumable productrepresented in at least one of the plurality of images, determine a typeindicator associated with the detected consumable product. The typeindicator may be determined by analyzing one or more of the plurality ofimages. A type indicator may be an indication of the brand name,category, or other classification of the consumable product. Forexample, the type indicator associated with a detected consumableproduct may be food, beverage, alcoholic drink, pill, cigarette, orcigar. The type indicator may be more specific. For example, the typeindicator associated with a detected consumable product may be soup,pasta, salad, bread, cake, dessert, vegetable, or meat. As may beappreciated from this disclosure, the type indicator may be an indicatorassociated with any level of categorization.

In some embodiments, the at least one processing device may beconfigured to determine the type indicator associated with the detectedconsumable product by comparing at least a portion of a plurality ofimages to stored images of known consumable product types. For example,an image representing a detected consumable product may be compared toimages of known consumable products. The comparison may be carried outin substantially the same manner as described above. The images of theknown consumable products may be stored on wearable apparatus 110, oncomputing device 120, or on another device in communication withwearable apparatus 110 or computing device 120. Additionally oralternatively, images of known consumable products may not be stored ina memory, but may be access by a search performed on the Internet, anintranet, or other online service. In some embodiments, the detectedproduct may be determined to be of the type of the known consumableproduct represented in the image that most closely matches the image ofthe detected product. For example, a detected product may be compared toimages depicting a hamburger, a cheeseburger, a chicken burger, and agrilled cheese, and may be determined to be, for example, a cheeseburgerbased on the image representing the detected product most closelymatching the image of the cheeseburger.

In some embodiments, the at least one processing device may compare animage of a detected product to a plurality of images of known productsin a number of rounds to determine a type of the detected product. Thenumber of comparisons performed may depend on the level of specificityrequired or desired. For example, in the example of image 1800, wearableapparatus 110 may compare the representation of object 1808 (which waspreviously determined to he a consumable product on plate 1902) to aplurality of images representing a plurality of consumable products onplates. This first comparison may lead to a determination that object1808 is a mashed consumable product. Wearable apparatus 110 may thencompare object 1808 to a plurality of images of mashed consumableproducts. This comparison may lead to a determination that object 1898is a mashed vegetable product. Wearable apparatus 110 may then compareobject 1808 to a plurality of images of mashed vegetable products and,based on this comparison, ultimately determine that object 1808 is apile of mashed sweet potatoes.

In some embodiments, the at least one processing device may beconfigured to use information relating to the detected product todetermine a plurality of images to use for comparison in determining atype of the product. The information may be used to determine one ormore keywords and the keywords may be used to locate images of knownconsumable products relating to the detected consumable product. Thekeywords may be determined as described above. As an example, wearableapparatus 110 may detect liquid 1816 by detecting wine glass 1814, asdescribed above. Wearable apparatus 110 may then generate keywordsrelated to wine glasses to search for images of consumable liquids inwine glasses. The images of liquids in wine glasses may then be comparedto image 1800 (or the portion thereof containing wine glass 1814) todetermine a type associated with liquid 1816. For example, afterrelatively few comparisons, wearable apparatus may determine that liquid1816 is a Merlot based on its visual appearance matching that of animage of Merlot. This is advantageous because the at least oneprocessing device may avoid unnecessary comparisons, such as acomparison of liquid 1816 to soup or to milk. It is contemplated thatadditional information may be used to generate keywords to furthersimplify the comparison process. Continuing the above example, wearableapparatus 110 may identify liquid 1816 as having a purplish-red color,which may be used to limit the images used for comparison to images ofred wines, thereby preventing the use of time and processing powercomparing liquid 1816 to white wines or other liquids that are unlikelyto match liquid 1816. As another example, wearable apparatus 110 mayidentify a particular item (e.g., food or beverage) based on one or moreimages captured by wearable apparatus 110 that include a menu. Forexample, wearable apparatus 110 may use OCR techniques to identify textfrom the memory and/or may analyze captured audio data of the userplacing an order.

In some embodiments, the detected product may not closely match any ofthe images of known consumable products. In this instance, the at leastone processing device may be configured to determine the type of thedetected consumable by identifying ingredients making up the detectedconsumable product. For example, if wearable apparatus 110 is not ableto determine that cheeseburger 1902 is a cheeseburger by comparing image1900 with images of cheeseburgers, it may determine that cheeseburger1900 is a cheeseburger based on detection of the ingredients that makeup cheeseburger 1902. In this example, wearable apparatus 110 maydetermine that an image of lettuce corresponds with a portion component1906, an image of tomato corresponds with component 1910, and an imageof sliced cheese corresponds with component 1908. Based on thisincomplete identification, wearable apparatus 110 may determine thatcheeseburger 1902 is neither lettuce, nor tomato, nor sliced cheese, butthat it is something containing those ingredients. It is contemplatedthat the at least one processing device may then search for images ofknown consumable products containing the three determined ingredientsand use those images for further comparison with image 1900. Thecomparisons may lead to a determination that cheeseburger 1902 is acheeseburger. Alternatively, the comparisons may lead to furtheridentification of ingredients. For example, the at least one processingdevice may determine that component 1912 is a meat patty, component 1904is a top bun, and component 1914 is a bottom bun. The process ofidentifying ingredients and using those ingredients to generate a set ofimages to compare with the detected consumable product helps determinethe type of a consumable product.

It is contemplated that the at least one processing device may beconfigured to use a neural network to determine a type of product from alist of determined ingredients. For example, the neural network mayinclude a first layer configured to receive a plurality of ingredients,at least one hidden layer, at an output layer configured to identify themost likely type of consumable product associate with the ingredients.For example, if the input to the neural network includes the list oflettuce, tomato, sliced cheese, and meat patty, the output from theneural network would be a proper identification of cheeseburger 1902 asa cheeseburger.

It is also contemplated that if a type of the consumable product may notbe directly determined by the image analysis, the identification ofingredients may be used to determine feedback related to the consumableproduct, as discussed further below. It is also contemplated that the atleast one processing device may be configured to seek input from user100 when ingredients are identified but not an ultimate type of theconsumable product. For example, the at least one processing device maygenerate instructions for causing a device to display image 1900 and thelist of ingredients including lettuce, tomato, sliced cheese, and meatpatty, and to request input form user 100 regarding the proper type. Adevice may then be configured to perform the operations of theinstructions and to receive the input. The input may then be saved withthe image, e.g. image 1900, for later use in comparison to othercaptured images.

It is further contemplated that the at least one processing device maybe configured to determine the ingredients of a consumable product afterthe type of the product has been determined. Identification ofingredients may be accomplished by comparison of the determinedconsumable product with known consumable products of the same type,wherein the known consumable products have varying ingredients. Forexample, if wearable apparatus 110 determined that cheeseburger 1902 isa cheeseburger by comparing it with images of known cheeseburgers, itmay perform further operations to determine the ingredients ofcheeseburger 1902. In this example, the processing device may beconfigured to compare a plurality of images of cheeseburgers havingdifferent ingredients to image 1900. As an example, the processingdevice may compare image 1900 with an image of a cheeseburger havingonion, a cheeseburger having no additional toppings, a cheeseburgerhaving two meat patties, a cheeseburger having a turkey-based meatpatty, a cheeseburger having a sesame-seed bun, a cheeseburger having awhole-wheat bun, and so forth. The ingredients making up cheeseburger1902 may then be identified in substantially the same manner asdescribed above. The ingredients that make up a consumable product maybe used for determining feedback related to the consumable product, asdescribed further below.

In some embodiments, the at least one processing device may beconfigured to determine the type indicator associated with the detectedconsumable product based, at least in part, on detection of a labelassociated with packaging of the consumable product and recognition oftext appearing on the detected label. The text may be, for example, alogo, a description of the product, a name of the product, and so forth,for example, the image of the consumable product may contain a wrapperor other packaging associated with the consumable product. It iscontemplated that the at least one processing device may be configuredto identify the packaging and detect text thereon (e.g., using an OCRprogram) and use the text to determine the type of the consumableproduct. For example, the detected consumable product may be associatedwith a detected box that contains the text “Big Mac®” and wearableapparatus 110 may determine that the defected consumable product is acheeseburger based on this information. Matching the text “Big Mac®”with the type “cheeseburger” may be accomplished in substantially thesame manner as the image comparison described above. For example, animage known to contain a cheeseburger and also containing the words “BigMac®” may be matched with the image of the detected consumable and thecorresponding text may be used to determine that the detected consumableproduct corresponds with the known cheeseburger. It is also contemplatedthat wearable apparatus 110 may match the text “Big Mac®” with acheeseburger by supplying the term “Big Mac®” as a keyword for a searchquery, as described above, and determining based on the search resultsthat the detected consumable product is a cheeseburger. Additionally oralternatively, the processing device may be configured to determine thetype without using image comparison. For example, the keyword search mayreturn nutritional information or a description of a “Big Mac®,” whichis sufficient to determine the consumable product is a cheeseburgerwithout requiring additional analysis.

In another example, the at least one processing device may be configuredto use the text detected in an image as a keyword to determine the typeof the detected consumable product. This may be advantageous when thetext detected in the image is not directly associated with a consumableproduct type (e.g., the “Big Mac®” example above) but describes aquality or characteristic of the type of the consumable product. Thekeyword may be used to search for images of related consumable products,as described above, or otherwise used to determine a type of theconsumable product. Using the keyword to search for images of knownconsumable products may reduce the pool of images used for comparisonwith a detected consumable product thereby reducing the processing time.For example, the at least one processing device may detect the word“Atlantic” in an image containing a detected consumable product, or inan image related to the image containing the detected consumableproduct, and per form a search for images of consumable productsassociated with the word “Atlantic.” The images returned from the searchmay include various seafood products sourced from the Atlantic Ocean,which may be compared with the image of the detected consumable productas described above, to determine a type of the detected consumableproduct. In other examples, the detected word may be related to a commondescriptor of types of consumable products, such as “spicy,” “juicy,”“salty,” “sweet,” “savory,” and so forth. It is contemplated that one ormore keywords may be used in each search. Using more than one keywordmay produce more relevant images of known consumable products andimprove the likelihood of determining the type of the detectedconsumable product with fewer comparisons. xx

In another example, wearable apparatus 110 may detect a menu from whichuser 100 orders a meal. It is contemplated that wearable apparatus 110may additionally detect an indication of which menu option user 100ordered, for example, by detecting audio of user 100 conveying the orderto a server. Based on this information, wearable apparatus 110 maydetermine that a later detected consumable object is of the type orderedby user 100. For example, wearable apparatus 110 may capture an image ofa menu at a first time and capture an image of a consumable product at asecond time. Based on the menu and consumable product being captured ina similar location and in temporal proximity, the at least oneprocessing device may determine that the consumable product correspondswith a product listed on the menu. In this example, the at least oneprocessing device may compare the image of the detected consumableproduct with an image of each product listed on the menu to determinethe type of the consumable product.

In some embodiments, the at least one processing device may use acombination of a keyword search and the image comparison to determine atype of consumable product. This may be advantageous when, for example,several types of consumable products have a similar visual appearance.For example, a comparison of an image of a detected consumable productwith a plurality of images of known consumable products, as describedabove, may be effective for determining that a consumable product is,for example, a glass of milk. However, a glass of milk may havesubstantially the same appearance if it is whole milk or 2% milk. Inthis example, detection of a keyword, such as “whole” in a menu oringredients list, or as a spoken word may be used in addition to thevisual classification to determine that the detected consumable productis a glass of whole milk. The combined visual and textual or vocalidentification described here may be particularly advantageous fordistinguishing between consumable products of the same type havingdifferent ingredients or qualities, such as organic and non-organicproduce, gluten-free and gluten-containing grains, original or fat-freeproducts, grass-feed or corn-feed meats, and so forth. It iscontemplated that the visual identification and the textual or vocalidentification may be performed at substantially the same time or in anysequence.

Consistent with this disclosure, the at least one processing device maybe configured to analyze one or more of the plurality of images toestimate an amount of the consumable product consumed by a user. Theestimated amount may he measured as a volume, a percentage, a number ofconsumption events, and so forth. For example, if the type of theconsumable product is a food, the estimated amount may be a number ofspoonfuls, handfuls, units, bites, or the like. In another example, ifthe type of the consumable product is a beverage or drink, the estimatedmount may be volume, a number of sips, glasses, bottles, cans, or thelike. In another example, if the type of the consumable product is adrug, the estimated amount may be a number of pills, injections, sips,or the like. In another example, if the type of the consumable productis a cigarette, the estimated amount may be a number of puffs,cigarettes, cartons, or the like. It is contemplated that the estimatedamount may be any other quantifier of a consumable product and may varydepending on the type of the consumable product.

In some embodiments, the estimated amount of a consumable productconsumed may be determined by comparing the amount of consumable productdetected at a first time with an amount detected at a second time. Theamount present at each time period may be represented as a dimension ofthe consumable product, a number of items of the consumable product, apercentage of the consumable product, or the like. For example, ifwearable apparatus 110 captures image 1800, the amount of broccoli 1806present may be represented as 13 broccoli crowns, the amount of steak1804 present maybe represented as 5.5 inches wide by 8.5 inches long and1.25 inches thick, the amount of wine 1816 present may be represented as100% of a traditional wine-pour, and so forth. The amount of consumableproduct present at a second time may be similarly detected and theamount consumed by a user may be determined by calculating thedifference between the amount present at the first time and the amountpresent at the second time. Continuing the example, later during a meal,wearable apparatus 110 may capture an image substantially similar toimage 1800 and detect an amount of each consumable product present atthe second time. For example, the amount of broccoli 1806 present at thesecond time may be represented as 5 broccoli crowns, the amount of steak1804 present may be represented as 2.5 inches wide by 3 inches long by1.25 inches thick, the amount of wine 1816 present may be represented as20% of a traditional wine-pour, and so forth. The at least oneprocessing device may then estimate the amount of each consumableproduct consumed. For example, the amount of broccoli 1806 consumed maybe represented as eight broccoli crowns, the amount of steak 1804consumed may be represented as 3 inches wide by 5.5 inches long by 1.25inches thick, the amount of wine 1816 consumed may be represented as 80%of a traditional wine-pour, and so forth. The at least one processingdevice may perform additional operations to estimate the amount ofconsumable product consumed in a different unit of measure. For example,the measurements of steak 1804 consumed (i.e., 3 inches wide by 5.5inches long by 1.25 inches thick) may be compared to a graph, chart, orother source to determine how many ounces of steak 1804 were consumed.The graph, chart, or other source may be accessed through a search insubstantially the same manner as described above in relation to thekeyword searches.

In some embodiments, the amount of consumable product consumed by a usermay additionally or alternatively be calculated based on a comparison ofthe consumable product with another consumable product or anon-consumable product. For example, rather than comparing the amount ofsteak 1804 present at a first time with the amount of steak 1804 presentat a second time to determine the amount of steak 1804 consumed, the atleast one processing device may compare steak 1804 with plate 1802 ateach time period. The dimension of plate 1802 serves as a constant andmay be a more effective point of comparison for estimating a consumedamount. For example, steak 1804 may occupy 35% of plate 1802 in image1800 and may occupy 15% of plate 1802 in an image substantially similarto image 1800 captured at a second time. The at least one processingdevice may then use the dimensions of plate 1802 to determine an amountof steak 1804 consumed. This may be a particularly effective means forestimating the amount of consumable product consumed when the consumableproduct depends upon another object for its dimension. For example, theamount of wine 1816 consumed may be best determined by comparing theamount of wine 1816 present at a first time with wine glass 1814 andcomparing the amount of wine 1816 present at a second time with the samewine glass 1814. It is further contemplated that the consumable productmay be compared to an object unrelated to consumption to determine theamount consumed. For example, the pile of mashed sweet potatoes 1808 maybe compared to candle 1824 at both the first and second time period.

Consistent with this disclosure, the at least one processing device maybe configured to analyze a plurality of images captured in sequence toestimate an amount of consumable product consumed. For example, wearableapparatus 110 may be configured to capture an image in response to everyconsumption indicator, as described above, and capture a series ofimages related to a single consumption event, such as a meal. Forexample, an image may be captured each time a user's hand approaches themouth of the user, or another consumption indicator occurs.

In some embodiments, the estimated amount of consumable product consumedmay be calculated as a number of bites, sips, spoonfuls, handfuls,pieces, pills, or other unit. The number of units consumed may bedetermined by any means consistent with this disclosure. For example,wearable apparatus 110 may be configured to capture an image in responseto each consumption indicator, as discussed above, and the number ofunits may be calculated by counting the number of limes user 100 broughta unit of consumable product to his or her mouth. In another example,wearable apparatus 110 may be configured to capture an image at aninitial time and at a second time, as discussed above, and the number ofunits consumed may be calculated based on the estimated amount consumedand a known or determined average unit size.

In some embodiments, the at least one processing device may beconfigured to access a database to determine the standard or recommendedserving size for a type of consumable product and determine the amountof consumable product consumed as an amount of servings consumed. Astandard or recommended serving size is often expressed in standardunits, such as units, grains or ounces, and information relating theretois readily available online. For example, a keyword search performed asdescribed above may locate information relating to a standard servingsize. The at least one processing device may then determine the amountof consumable product as a function of the standard serving size. Forexample, if the standard serving size of a consumable product is 4ounces and the processing device determines the user consumed 6 ouncesof the consumable product, the processing device may determine that theuser consumed 1.5 servings of the consumable product.

It is contemplated that one or more of the several methods of estimatingan amount of consumable product consumed by a user may be performed atsubstantially the same time or that they may be performed in anycombination. For example, the at least one processing devices maydirectly compare the dimension of a consumable product present at afirst time with the dimension of the consumable product present at asecond time and compare the dimension at each time to the dimension ofone or more other objects. For example, the image 1800 captured at afirst time may be analyzed to determine the dimension of steak 1804 andplate 1802 and a later image substantially similar to image 1800 may beanalyzed to determine the remining dimension of steak 1804 and thedimension of plate 1802. This may allow for a more precise comparisonbecause the image captured at the second time may be modified such thatthe dimension of plate 1802 is the same at each time period, therebyensuring that the dimension of steak 1804 at the second time directlycorresponds with the dimension of steak 1804 at the first time. Othercombinations are possible, and this example is not limiting.

It is contemplated that the at least one processing device may usenon-consumable products or objects to facilitate comparison of a firstand second image of a consumable product when determining the amount ofconsumable product consumed. For example, when comparing an image of aconsumable product captured at a first time and an image of a consumableproduct captured at a second time, a non-consumable product present ineach image may be used to modify either the first or second image toallow for direct comparison between the two images. For example, image1800 may be captured at a first time and a second image may be capturedat a second time. The second image may contain all or most of theproducts present in image 1800, but may have been captured from adifferent angle or distance or under different conditions. If bothimages contain candle 1824 and table edge 1832, the at least oneprocessing device may tilt, zoom, crop, or otherwise modify the secondimage until, for example, candle 1824 is of the same size in each imageand table edge 1832 is in a similar position in each image. Once thesecond image has been modified, the amount of consumable product may beestimated as described above.

Consistent with this disclosure, the at least one processing device maybe configured to analyze for plurality of images to estimate a rate atwhich the detected consumable product is consumed by the user. The rateof consumption may be presented as a function of any amount ofconsumable product consumed over any period of time in which theconsumption occurred. It is contemplated that the rate of consumptionmay be estimated at substantially the same time as the amount ofconsumable product consumed is calculated. For example, when the amountis determined by comparing an amount of consumable product present in animage captured at a first time with the amount of consumable productpresent at a second time, as described above, the rate of consumptionmay be calculated by dividing the determined amount by the differencebetween the first and second time. In another example, when the amountis determined as a number of units consumed, as described above, therate of consumption may be determined by dividing the number of units bythe difference between the first and second time.

Consistent with this disclosure, the at least one processing device maybe configured to determine a feedback based on the on the detectedconsumable product. The feedback may include visual feedback, audiblefeedback, tactile feedback, a combination thereof, or the like. Thefeedback may include any information of interest to a user. The feedbackmay include any information generated or obtained by the at least oneprocessor by any means disclosed above. The at least one processingdevice may generate instructions for causing the feedback to beoutputted to a user. The instructions may be generated by, for example,processor 210 in wearable apparatus 110, processor 540 in computingdevice 120, an external processing device, or a combination thereof. Thefeedback may include any combination of audible, visible, or tactilefeedback. In some embodiments, the feedback may be output by feedbackoutputting unit 230. For example, the instructions generated byprocessor 210 or processor 540 may be received by feedback outputtingunit 230 and feedback outputting unit 230 may be configured to providethe feedback to user 100.

In some embodiments, the feedback may be output by wearable device 110.For example, the feedback may include tactile feedback, and wearableapparatus 110 may be configured to vibrate in response to receiving theinstructions. In another example, the feedback may include audiblefeedback and wearable apparatus 110 may be configured to perform thefeedback, for example, by playing it over a speaker or throughheadphones. In another example, the feedback may be visual feedback andwearable apparatus 110 may be configured to display the feedback, forexample, on a display in wearable apparatus 110 or by displaying thefeedback on lens of glasses 130. If the feedback is tactile feedback,apparatus 110 may be configured to vibrate when it is determined thatthe user has consumed a predetermined amount of one or more consumable,has received a predetermined amount of a nutrient, or the like. In someembodiments, the feedback may be output by computing device 120. Forexample, computing device 120 may be configured to vibrate or performaudible feedback when it receives the feedback instructions fromwearable apparatus 110 and or another device (e.g., a remote server), inanother example, computing device 120 may be configured to display thefeedback, for example, by displaying visual feedback on display 260).

In some embodiments, causing the feedback to be output may includetransmitting the feedback to a device paired with the wearableapparatus. The device may include any device disclosed herein or anyother device capable of outputting the feedback. For example, the devicemay include one of a smartphone, a tablet, or a smartwatch. It iscontemplated that an application installed on the device may beconfigured to receive the instructions and cause the feedback to beoutput. The feedback or the instructions for causing the feedback to beoutput may be transmitted to the device by any means, for example, viaWiFi over a network, via the Internet, via Bluetooth®, etc.

The feedback may relate to a consumable product or a type thereof. Forexample, if the ingredients of a consumable product are determined asdescribed above, the feedback may include information relating to one ormore ingredients. For example, after consumption of cheeseburger 1902,the feedback may include: “you consumed a cheeseburger, which contained,a bun, a meat-patty, lettuce, two tomatoes, and one slice of cheese.”For example, the feedback may include the identity of the type ofconsumable product or the estimated amount of consumable productconsumed. For example, after analyzing image 1900, the at least oneprocessing device may provide visual feedback including, “you consumed acheeseburger.” The feedback may include additional information relatedto the consumable, such as the location or time at which the consumableproduct was consumed. For example, the feedback may include “youconsumed a cheeseburger at 12:15 PM.”

In some embodiments, the feedback may be based on a type of the detectedconsumable product and an estimated amount of the consumable productconsumed by a user. The type of the detected product and the amountconsumed may be determined by any means disclosed herein. The feedbackmay include an identifier of the type of consumable product and theamount of consumable product consumed. For example, after analyzingimage 1800 as described above, the at least one processing device mayprovide feedback including: “you consumed 8 broccoli crowns” or “youconsumed 2 glasses of wine.” In some embodiments, the feedback may bebased on the estimated rate at which the detected consumable product isconsumed. For example, the at least one processing device may determinea rate of consumption as described above and may use data related to therate to determine a feedback. The feedback may be determined bycomparison to user preferences, established standards, of the like ormay include a general description of the rate. For example, feedbackbased on a rate may include: “you consumed 3 alcoholic drinks in 2hours” or “you consumed 2,500 calories in 1 day, the recommended dailycaloric intake is 2,000 calories” or the like.

In some embodiments, the feedback may include data derived frominformation generated by the at least one processing device. The deriveddata may, for example, use information relating to the type and amountof consumable product consumed to determine information of interest to auser. The at least one processing device may be configured to accessvalues stored on a database, network, Internet, intranet, or device anduse that data to determine the feedback. For example, the feedback maybe determined based on values stored for the detected consumableproduct. The values may include nutritional values, recommended values,or other values related to consumption, such as the amount of calories,carbohydrates, fat, proteins, vitamins, micronutrients, alcohol,nicotine, or the like associated with the consumable product. Forexample, upon determining that consumable product 1804 is a steak, theat least one processing device may search for nutritional values forsteak 1804 and the feedback may include the nutritional information. Forexample, the feedback may include: “you consumed a steak, which has 61calories, 4 grams of fat, 0 grams of carbohydrates, and 5.7 grams ofprotein per serving.” Additionally or alternatively, the amount ofconsumable product may be used to determine further feedback, such as anestimation of consumed calories, consumed carbohydrates, consumed fat,or consumed protein. Continuing the above example, the amount of steak1804 consumed may be used to determine how many servings user 100consumed, as described above, and the feedback may include: “youconsumed 1.5 servings of steak, which includes 91.5 calories, 6 grams offat, 0 grams of carbohydrates, and 8.6 grams of protein” or “youconsumed 91.5 calories, 6 grams of fat, and 8.6 grams of protein” or thelike. In a similar example, the consumed product may be an alcoholicdrink and the feedback may relate to an amount of consumed alcohol. Forexample, the at least one processing device may search for the alcoholcontent for a type of product and use that information to determine atotal amount of alcohol consumed. The feedback determined in thisexample may include: “you consumed 4 glasses of wine,” “you consumed 4glasses of Merlot, which is 14.5% alcohol,” or the like.

In some embodiments, the determined feedback may include a comparison toa recommended amount of consumable product. A recommended amount may bean amount included in for example, a typical 2,000 calorie diet, arecommended number of servings for a category of food, a surgeongeneral's alcohol safety warning, a medical association's recommendeddaily intake, and so forth. For example, in the example above, thenutritional information for steak 1804 may include a percentagebreakdown of the recommended daily intake of each nutrient or the atleast one processing device may be configured to determine a percentdaily value. For example, the feedback related to steak 1804, asdisclosed above, may include: “you consumed 91.5 calories, which is 4.6%of the recommended 2,000 calorie diet.” In another example, the feedbackdetermined for wine 1816, as disclosed above, may include; “you consumed4 glasses of Merlot, the World Health Organization recommendsconsumption of no more than 2 alcoholic drinks a day.”

In some embodiments, the feedback may include information relating to acomparison between the determined consumption and user preferencesrelating to consumption. A user may set goals related to consumption,such as goals intended to aid them in losing or gaining weight, quittingsmoking, drinking fewer alcoholic drinks a day, and so forth. It iscontemplated that wearable apparatus 110 and/or computing device 120 maybe configured to receive and store user preferences, for example inmemory 550 andor 550 b. The at least one processing device may thencompare the determined amount and/or type of consumable product with theuser preferences to determine a feedback. For example, a user preferencemay indicate that user 100 desires to consume less than 150 grams ofcarbohydrates per day. In this example, feedback may include a messagecontaining, “you consumed 100 grams of carbohydrates, which is 66% ofyour daily goal.”

In some embodiments, the feedback of the instructions for generating thefeedback may be transmitted to one or more devices. For example, thefeedback may be transmitted to a first device for storage as describedabove and transmitted to a second device for outputting to a user. Insome embodiments, the feedback may be transmitted to a server, such asserver 250. The feedback may be transmitted by, for example, wirelesstransceiver 530 in wearable apparatus 110.

In some embodiments, feedback or the instructions for generating thefeedback may be stored. For example, at least one processing device maybe configured to store the feedback in a memory device. The memorydevice may be any memory device in communication with the processingdevice, such as memory 550, 550 b, a database, or another memory device.The stored feedback may include at least one value that is aggregatedover time. The value that is aggregated over time may be any that is ofinterest to a user, as defined by user preferences, or any that isdetermined to be of importance. For example, the at least one value mayrelate to a number of calories consumed by the user over a particulartime period, an amount of alcohol consumed over a particular limeperiod, a number of pills consumed over a particular time period, or thelike. The time period may be, for example, one or more hours, one ormore days, one or more weeks, one or more months, and so forth.

Stored feedback may be used to determine additional feedback or futurefeedback. In some embodiments, the stored feedback may includeinformation relating to the captured image underlying the feedback andmay be used in a later determination of a type or amount of consumableproduct consumed by a user. For example, a captured image may becompared with an image associated with the stored feedback, as describedabove, and the at least one processing device may determine that thecaptured image contains the same consumable product as the imageassociated with the stored feedback. The at least one processing devicemay, based on the determination, further determine that feedbacksubstantially similar to the stored feedback is appropriate for thecaptured image. In some embodiments, the stored feedback may be used todetect patterns, trends, or other information and provide additionalfeedback related to the information. For example, the stored feedbackmay include one or more aggregated values, as described above, and theat least one processing device may compare the aggregate values to userpreferences, recommended intake values per day or another time period,predetermined values, or other metrics to generate additional feedback.For example, the aggregated value may relate to total calories consumedin a particular time period and the additional feedback may include anindication of how the value compares to a user's caloric intake goals asdefined by a user preference. In another example, the at least oneprocessing device may compare the aggregate value for a first timeperiod with the aggregate value for a second time period and generatefeedback based on the comparison. For example, it is contemplated that,at the end of each week, the processing device may compare an aggregatevalue for that week with a related aggregate value for the proceedingweek or weeks and provide feedback relating to the comparison. Thefeedback man include, for example, “this week you consumed 1,321 fewercalories than you consumed last week” or “last week you consumed 15,759calories; this week you consumed 14,438” or the like. It is contemplatedthat the feedback may include a graphic representation of thecomparison, such as, for example, a pic graph, line graph, bar chart,table, or other representation of the relationship between the valuescorresponding with a plurality of time periods.

In some embodiments, feedback may be generated in response to a userinput requesting information related to consumption. For example, a usermay request information relating to a consumable product (e.g., thenutritional values related thereto, the amount consumed, the rate ofconsumption, etc.), to a consumption event (e.g., a meal, snack, bite,etc.), a time frame (e.g., consumption over an hour, day, week, etc.), acombination thereof, or any other information related to consumption.The feedback may then be generated as disclosed herein or, if previouslygenerated, accessed from a storage device as discussed above. It iscontemplated that a user may request feedback on an ad hoc basis or may,through a user preference, request feedback on a set schedule. Forexample, one or more user preferences may indicate that user 100 desiresfeedback substantially continuously (e.g., as a consumable product isconsumed); only when consumption inconsistent with a user preference isdetermined (e.g., when the processing device determines the userconsumed too many calories, drinks, carbohydrates, etc.); after everymeal, day, week, month, or other time frame; or when any other conditionis met. It is further contemplated that a user may dictate the type offeedback he or she wishes to receive. For example, user 100 may, on anad hoc basis or through user preferences, dictate that he or she wishesto receive feedback containing a comparison of the consumption occurringbetween a first and second time frame, a comparison of a consumptionevent with user preferences, a nutritional breakdown of a consumableproduct, and so forth.

In some embodiments, the at least one processing device may beconfigured to determine a type of feedback a user wishes to receive andto provide that feedback. For example, if user 100 requests a particulartype of feedback, as described above, under the same or similarconditions one or more times, the processing device may automaticallygenerate and provide that type of feedback when those conditions arepresent. For example, if user 100 request feedback between 9 PM and 10PM and request that the feedback contain a daily breakdown of consumednutrients, the processing device may, absent a subsequent request, beginautomatically providing a daily breakdown of consumed nutrients between9 PM and 10 PM. It is also contemplated that rather than automaticallyproviding feedback consistent with a detected pattern or trend, the atleast one processing device may provide feedback relating to thedetected pattern or trend. Continuing the example, rather thanautomatically providing a daily breakdown of consumed nutrients, the atleast one processing device may provide an output prompting the user toinclude the daily breakdown in his or her user preferences.

In some embodiments, the processing device may be configured to providefeedback before consumption. For example, the feedback may be providedas soon as the consumable product is detected. In this example, thefeedback may include an indication of the nutritional value of thedetected consumable products. As an example, if wearable apparatus 110captures image 1800, it may provide feedback relating to the calories,fat, protein, carbohydrates, and other nutritional value for each ofsteak 1804, broccoli 1806, mashed sweet potatoes 1808, dessert 1822,wine 1814, and the like. The feedback may be displayed to user 100 byany means disclosed herein. Displaying the feedback as part of a head-upaugmented reality display may be particularly advantageous for thisembodiment because it enables user 100 to visualize the nutritionalimplications of his or her dietary choices.

Consistent with this disclosure, the at least one processing device maydetermine a recommendation based on the monitored consumption. Therecommendation may be provided to a user 100, for example, as part ofthe feedback provided to a user as described above. For example, thefeedback may include a recommendation associated with the detectedconsumable product. The recommendation may include any informationrelated to the consumable product, to one or more user preferences, toone or more health standards, and so forth.

FIG. 20 is an exemplary flowchart of a method 2000 for automaticallymonitoring the consumption of a user and providing feedback relating tothe consumption to a user. It is understood that method 2000 isexemplary only and the method performed by wearable apparatus 110 may besubstantially similar to that of method 2000, may include only portionsof method 2000, or may include additional steps not shown in method2000. In some embodiments, the steps of method 2000 may be performed bywearable apparatus 110 and one or more external devices (e.g., aprocessor included in an external server that receives data fromwearable apparatus 110 over a network and/or a processor included in anexternal device such as a laptop, smartwatch, smartphone, tablet,earphones, etc.). In other embodiments, method 2000 may be performed bya general-purpose computer or a special-purpose computer built accordingto embodiments of the present disclosure.

Method 2000 may include a step 2010 for capturing one or more inputsfrom an environment of a user. The input may be captured by a visualcapture device, audio capture device, or other devices configured tocapture images, videos, audio signals, or the like. For example,capturing the one or more images may include capturing one or moreimages from at least one image capture device of wearable apparatus 110.In some embodiments, the images may be part of a video stream.

Method 2000 may include a step 2012 for analyzing the captured images orvideos to detect a consumable product represented in at least one of thecaptured images. The consumable product may be detected by any meansdisclosed herein. For example, image analysis as described above may beused to determine whether particular objects are consumable or othernon-consumable objects. Further, the analysis may include determiningthrough image analysis whether a consumable product was eaten andwhether the person who ate the consumable product was a user of thewearable apparatus. For example, the images captured by the wearableapparatus may be analyzed to determine that a band holding a consumableproduct is a hand associated with the user of the wearable apparatus orthat a plate of food or food packaging in an image is positioned in fromof the user of the wearable apparatus.

Method 2000 may include a step 2014 for determining a type of thedetected consumable product. The type of the detected consumable productmay be determined by any means disclosed herein. For example, throughimage analysis, an identifier of the detected consumable product, suchas a generic product name or a brand name may be determined as discussedabove.

Method 2000 may include a step 2016 for estimating an amount of theconsumable product consumed by the user. The amount of consumableproduct may be determined by analyzing the captured images, for example,by detecting an amount of consumable product present at a first time anddetecting an amount of consumable product present at a second time, thedifference between the two amounts representing the amount consumed bythe user. In some embodiments, method 2000 may include a step (notshown) for estimating a rate at which the detected consumable productwas consumed by the user. Still further, in some embodiments, the amountof consumable product may be determined by counting a quantity of theconsumable product consumed by the user of the wearable apparatus (e.g.,the user ate three pancakes and drank two cups of coffee).

Method 2000 may include a step 2018 for determining a feedback based onthe type of consumable product consumed and the amount of the consumableproduct consumed. The feedback may include any feedback disclosedherein. In some embodiments, where method 2000 includes a determinationof the rate of consumption, determining the feedback may includedetermining feedback based on the rate of consumption in addition to theamount and type of consumable product consumed. Method 2000 may includea step 2020 for generating instructions for causing the feedback to beoutput to the user. The feedback may be output by any means disclosedherein. Method 2000 may include a step (not shown) for transmitting theinstructions. The instructions may be transmitted to a device and thedevice may output the feedback in response to receiving theinstructions. The instructions may additionally or alternatively betransmitted to wearable apparatus 110 and wearable apparatus 110 mayprovide audible or visual feedback to the user in response to receivingthe instructions. Method 2000 may include a step (not shown) for storingthe determined feedback, the generated instructions, or data obtained orgenerated during any step of method 2000). Additional examples offeedback are discussed below.

FIG. 21 is an exemplary flowchart showing a process 2100 for determininga recommendation related to consumption. Process 2100 may include a step2110 for receiving data relating to one or more of the type or amount ofconsumable product consumed. The data may be generated by the one ormore processors as described above. The data may be received directlyfrom one or more processor or may be accessed from a storage device orover a network.

Process 2100 may include a step 2112 lot aggregating or isolating one ormore values in the data. For example, the data may be stored asdescribed above and one or more values may be aggregated. Theaggregation may include saving data related to a plurality ofconsumption events including the date, time, consumable product, amountconsumed, rate of consumption, and so forth for each consumption event.In another example, the values in the data may be compared to othervalues, such as those in user preferences or health standards, and oneor more values may be isolated based on, for example, exceeding anamount indicated in the user preferences or health standards. Forexample, if the data indicates that user 100 consumed 200 grams ofcarbohydrates and the user preferences associated with user 100 indicatethat he or she intends to consume no more than 150 grams ofcarbohydrates a day, the 200 gram value may be isolated.

Process 2100 may include a step 2114 for comparing the aggregated orisolated data to stored data. The stored data may be, for example,guidelines, conditions, or rules for generating a recommendation. Thestored data may be stored on a memory, such as memory 550, 550 b, or ona database or other memory device. As an example, a stored rule mayindicate that if a processing device determines that a value of anutrient consumed by a user exceeds a threshold, then a recommendationshould be generated and output. The stored data may reflect one or morerecommendation rules. For example, a recommendation rule may dictatethat if a user consumes a nutrient in an amount within a threshold of apredetermined value, that a recommendation should be generated. Thecomparison in this example, would comprise of a comparison between theamount of nutrient consumed and the predetermined value. The stored datamay reflect a pattern determined or identified by the processing device.For example, the processing device may aggregate the data as discussedabove and may determine one or more consumption habits of the user, suchas a value of calories, pills, cigarettes, fats, protein, etc. that theuser consumes on a routine basis or a time frame in which the userconsumes the same. The comparison, in this example, comprises of acomparison between received data and the pattern. As may be appreciatedfrom this disclosure, the stored data may reflect any rules, guidelines,patterns, or other information useful for determining a recommendation.

Process 2100 may include step 2116 for generating a recommendation basedon the comparison. The recommendation may be generated in substantiallythe same manner as the instructions for causing the feedback to beoutput, as described above. One or more rules may dictate the content ofthe recommendation. For example, if the processing device determinesthat the user consumed more of a nutrient than is recommended by ahealth standard, a rule may dictate that the processing device recommendto the user that he or she consume less of that nutrient on a subsequentday. The recommendation may be generated in accordance with one or morerules or with a pattern detected by the processing device. For example,if user 100 consumes an apple every day, the processing device mayrecommend that user vary his or her consumption by, for example,consuming a pear or orange rather than an apple. The recommendation maybe generated in accordance with one or more advertising criteria, whichmay be provided by an advertiser. For example, an advertising criteriamay dictate that processing device recommend a product to every consumerwho consumes a specific type of consumable product.

Process 2100 may include a step 2118 for generating instructions foroutputting the recommendation. The instructions for outputting therecommendation may be generated in substantially the same mannerdisclosed with respect to generating the instructions for causing adevice to output the feedback. The instructions may include anindication of how, when, and where a recommendation is to be output aswell as the content of the recommendation. It is contemplated that theinstructions may be transmuted to an output device, stored in a memory,or processed by an outputting unit or processing device.

In some embodiments, the recommendation may be provided prior toconsumption of a detected consumable product. For example, wearableapparatus 110 may capture an image of a consumable product and the oneor more processing devices may determine that the consumable product isof a first type as described above, prior to consumption of theconsumable product, the processing device may provide a recommendationrelated to the type of consumable product. For example, if wearableapparatus 110 detects cheeseburger 1902, before user 100 begins eating,the at least one processing device may provide a recommendation relatedto cheeseburger 1902. The recommendation may include, for example, arecommendation that user 100 eat less than all of cheeseburger 1902. Inanother example, wearable apparatus 110 may detect a menu and provide arecommendation relating to what user 100 should order. For example, theat least one processing device may detect a menu and, in response,access stored data relating to what user 100 consumed prior to thedetection of the menu. If, for example, user 100 consumed relativelyunhealthy consumable products for breakfast and lunch, the at least oneprocessing device may recommend that user 100 order a low-caloric orotherwise healthy option from the menu.

In some embodiments, the recommendation may include a recommendation toswap a consumable product that a user frequently consumed with ahealthier alternative consumable product. For example, if the at leastone processing device determines that user 100 consumes a soft-drinkevery day (by analyzing aggregated consumption data, for example), theat least one processing device may be configured to determine analternative consumable product with similar characteristics to thesoft-drink and recommend to user 100 that he or she replace thesoft-drink with the alternative. In this example, the replacementproduct may be, for example, a diet soft-drink, a sports drink, a glassof water, a glass of tea, or the like. The at least one processingdevice may determine the healthier alternative by, for example,comparing the nutritional information of the detected consumable productwith the nutritional information of one or more consumable products ofthe same type. The nutritional information may be accessed through akeyword search, as described above, or from a storage device incommunication with the at least one processing device.

In some embodiments, the recommendation may include an advertisement.The advertisement may be provided to the at least one processing deviceby one or more advertisers. The advertisement may be provided to user100 as a recommendation if the predetermined criteria is met. Thepredetermined criteria may be provided by an advertiser or determined bythe at least one processing device, for example, the criteria mayprovide that if a certain brand of consumable product is detected, anadvertisement from a different brand that provides the same or similarconsumable product should be provided as a recommendation to user 100.For example, Coca-Cola® may provide an advertising criterion indicatingthat a Coke® advertisement should be displayed as a recommendation touser 100 whenever wearable apparatus 110 determined that user 100consumed a Pepsi® product. It is contemplated that the advertisementcriteria may include rules for providing an advertisement if any set ofconditions is met.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, orother optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A wearable apparatus for automatically monitoringconsumption by a user of the wearable apparatus, by analyzing imagescaptured from an environment of the user, the wearable apparatuscomprising: at least one image capture device configured to capture aplurality of images from an environment of the user of the wearableapparatus; and at least one processing device configured to: analyze theplurality of images to detect a consumable product represented in atleast one of the plurality of images; based on the detection of theconsumable product represented in at least one of the plurality ofimages, analyze one or more of the plurality of images to determine atype indicator associated with the detected consumable product; analyzethe one or more of the plurality of images to estimate an amount of theconsumable product consumed by the user; determine a feedback based onthe type indicator of the detected consumable product and the estimatedamount of the consumable product consumed by the user; and cause thefeedback to be outputted to the user.
 2. The wearable apparatus of claim1, wherein the at least one processing device is further configured to:analyze the plurality of images to estimate a rate at which the detectedconsumable product is consumed by the user; and determine the feedbackfurther based on the estimated rate at which the detected consumableproduct is consumed by the user
 3. The wearable apparatus of claim 1,wherein the type indicator associated with the detected consumableproduct is at least one of food, beverage, alcoholic drink, pill,cigarette, or cigar.
 4. The wearable apparatus of claim 1, wherein thetype indicator associated with the detected consumable product is atleast one of soup, salad, pasta, bread, cake, dessert, vegetable, ormeat.
 5. The wearable apparatus of claim 1, wherein the at least oneprocessing device is configured to determine the type indicatorassociated with the detected consumable product based, at least in partafter detection of a label associated with packaging of the consumableproduct and recognition of at least a portion of text appearing on thedetected label.
 6. The wearable apparatus of claim 1, wherein the typeindicator of the detected consumable product is food and the estimatedamount is a number of bites, spoonfuls, handfuls, or units.
 7. Thewearable apparatus of claim 1, wherein the type indicator of thedetected consumable product is beverage or drink and the estimatedamount is a number of sips.
 8. The wearable apparatus of claim 1,wherein the feedback provides an estimation of consumed calories,consumed carbohydrates, consumed fat, or consumed protein.
 9. Thewearable apparatus of claim 1, wherein the feedback relates to anestimation of an amount of consumed alcohol.
 10. The wearable apparatusof claim 1, wherein the type indicator of the detected consumableproduct is determined by comparing at least a portion of the one or moreof the plurality of images to stored images of consumable product types.11. The wearable apparatus of claim 1, wherein the feedback isdetermined based on values stored for the detected consumable product.12. The wearable apparatus of claim 1, wherein the feedback includes arecommendation associated with the detected consumable product.
 13. Thewearable apparatus of claim 1, wherein the feedback includes audiblefeedback or visual feedback.
 14. The wearable apparatus of claim 1,wherein the feedback is stored in a memory device.
 15. The wearableapparatus of claim 14, wherein the feedback stored in the memory deviceincludes at least one value that is aggregated over time.
 16. Thewearable apparatus of claim 15, wherein the at least one value relatesto calories consumed by the user over a particular time period.
 17. Thewearable apparatus of claim 15, wherein the at least one value relatesto an amount of alcohol consumed by the user over a particular timeperiod.
 18. The wearable apparatus of claim 15, wherein the at least onevalue relates to a number of pills consumed by the user over aparticular time period.
 19. The wearable apparatus of claim 1, whereincausing the feedback to be output includes transmitting the feedback toa device paired with the wearable apparatus.
 20. The wearable apparatusof claim 19, wherein the paired device includes one of a smartphone, atablet, or a smartwatch.
 21. The wearable apparatus of claim 1, whereinthe at least one image capture device is a video camera.
 22. A methodfor automatically monitoring consumption by a user of a wearableapparatus, the method comprising: capturing, by the wearable apparatus,one or more images from an environment of the user; analyzing the imagesto detect a consumable product represented in at least one of the one ormore images; determining a type of the detected consumable product;analyzing the images to estimate an amount of the consumable productconsumed by the user; determining a feedback based on the type of theconsumable product and the amount of the consumable product consumed bythe user; and generating instructions for causing the feedback to beoutput to the user.
 23. The method of claim 22, further comprising:analyzing the images to estimate a rate at which the detected consumableproduct is consumed by the user; and determining the feedback furtherbased on the estimated rate.
 24. The method of claim 22, whereindetermining the feedback comprises determining a recommendation relatedto the consumable.
 25. The method of claim 22, wherein determining thetype of the consumable product comprises comparing the one or moreimages to stored images of consumable product types.
 26. The method ofclaim 22, wherein determining the type of the consumable productcomprises detecting a label associated with packaging of the consumableproduct and identifying at least a portion of text appearing on thedetected label.
 27. The method of claim 22, wherein analyzing the imagesto estimate the amount of the consumable product consumed by the usercomprises determining a number of bites, spoonfuls, handfuls, units,sips, or pills consumed by the user.
 28. The method of claim 22, whereindetermining the feedback comprises comparing the amount of theconsumable product consumed by the user with a stored amount for thetype of consumable product and generating a recommendation based on thecomparison.
 29. The method of claim 22, further comprising: transmittingthe instructions to a device to cause the device to display thefeedback.
 30. The method of claim 22, further comprising: storing thefeedback in a memory device.